# pos

id: cord-103813-w2sb6h94
pos: NNS
token:
  https://www.cisa.gov/news/2020/05/13/fbi-and-cisa-warn-against-chinese-targeting-covid-19-research-organizations

lemma:
  https://www.cisa.gov/news/2020/05/13/fbi-and-cisa-warn-against-chinese-targeting-covid-19-research-organization


id: cord-198180-pwmr3m4o
pos: NNP
token:
  https://www.forbes.com/sites/louiscolumbus/2018/08/16/iot-market-predicted-to-double-by-2021-reaching-520b/#82674f91f948

lemma:
  https://www.forbes.com/sites/louiscolumbus/2018/08/16/iot-market-predicted-to-double-by-2021-reaching-520b/#82674f91f948


id: cord-198180-pwmr3m4o
pos: NNP
token:
  https://www.cisco.com/c/dam/en/us/products/collateral/se/internet-of-things/at-a-glance-c45-731471.pdf

lemma:
  https://www.cisco.com/c/dam/en/us/products/collateral/se/internet-of-things/at-a-glance-c45-731471.pdf


id: cord-198180-pwmr3m4o
pos: NNP
token:
  https://www.fda.gov/drugs/drug-safety-and-availability/fda-cautions-against-use-hydroxychloroquine-or-chloroquine-COVID-19-outside-hospital-setting-or

lemma:
  https://www.fda.gov/drugs/drug-safety-and-availability/fda-cautions-against-use-hydroxychloroquine-or-chloroquine-COVID-19-outside-hospital-setting-or


id: cord-198180-pwmr3m4o
pos: NNPS
token:
  https://talkbusiness.net/2020/04/groups-share-data-quantifying-COVID-19-impacts-on-trucking-industry/

lemma:
  https://talkbusiness.net/2020/04/groups-share-data-quantifying-COVID-19-impacts-on-trucking-industry/


id: cord-198180-pwmr3m4o
pos: NNP
token:
  https://spectrum.ieee.org/news-from-around-ieee/the-institute/ieee-member-news/thermal-cameras-are-beingoutfitted-to-detect-fever-and-conduct-contact-tracing-for-covid19

lemma:
  https://spectrum.ieee.org/news-from-around-ieee/the-institute/ieee-member-news/thermal-cameras-are-beingoutfitted-to-detect-fever-and-conduct-contact-tracing-for-covid19


id: cord-198180-pwmr3m4o
pos: JJ
token:
  https://www.fiercewireless.com/iot/at-t-4g-lte-connects-iot-robots-to-kill-germs-keep-shelves-stocked

lemma:
  https://www.fiercewireless.com/iot/at-t-4g-lte-connects-iot-robots-to-kill-germs-keep-shelves-stocked


id: cord-198180-pwmr3m4o
pos: NNP
token:
  https://spectrum.ieee.org/telecom/security/tracking-covid19-with-the-iot-may-put-your-privacy-at-risk

lemma:
  https://spectrum.ieee.org/telecom/security/tracking-covid19-with-the-iot-may-put-your-privacy-at-risk


id: cord-198180-pwmr3m4o
pos: NNP
token:
  https://www.govtech.com/opinion/If-Only-One-US-City-Wins-the-Smart-City-Race-the-Whole-Nation-Loses.html

lemma:
  https://www.govtech.com/opinion/If-Only-One-US-City-Wins-the-Smart-City-Race-the-Whole-Nation-Loses.html


id: cord-169484-mjtlhh5e
pos: NNP
token:
  https://www.derstandard.at/jetzt/livebericht/2000088169126/buwog-prozessvermoegensverwalter-stinksauer-auf-meischberger

lemma:
  https://www.derstandard.at/jetzt/livebericht/2000088169126/buwog-prozessvermoegensverwalter-stinksauer-auf-meischberger


# ent

id: cord-007708-hr4smx24
type: DNA
entity:
  4th Human Variome Project Meeting Comprehensive molecular characterization of urothelial bladder carcinoma Open clinical trial data


id: cord-007708-hr4smx24
type: CHEMICAL
entity:
  Man OMIM R ClinVar public archive of relationships among sequence variation and human phenotype Searching NCBI s dbSNP


id: cord-008584-4eylgtbc
type: ORG
entity:
  the National Influenza Reference Laboratory National Centre for Microbiology World Health Organization National Influenza Centre


id: cord-017634-zhmnfd1w
type: TAXON
entity:
  Bartonella henselae Borrelia burgdorferi Campylobacter Cryptosporidium Cyclospora Ebola virus Escherichia coli 0157 H7 Ehrlichia Hantaan virus


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entity:
  The Office of the National Coordinator for Health Information Technology Food and Drug Administration


id: cord-003243-u744apzw
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entity:
  Sequential Modelling of the Effects of Mass Drug Treatments on Anopheline Mediated Lymphatic Filariasis Infection


id: cord-016140-gvezk8vp
type: ORG
entity:
  the EU Product Liability Directive to Software Computer Software and Information Licensing in Emerging Markets


id: cord-018133-2otxft31
type: DISEASE
entity:
  Weaver 1976 Levitt 1983 The most important development to support the emergence of bioinformatics however


id: cord-016528-j7lflryj
type: FAC
entity:
  North Carolina Biosurveillance System Wiley Handbook of Science and Technology for Homeland Security Update


id: cord-009797-8mdie73v
type: CL
entity:
  grass prairie birds e g dickcissel and species associated with open country environments with scattered trees and shrubs e g eastern phoebe Besides these biogeographical patterns we also highlight the ability of our algorithm in depicting how environmental gradients are linked to the proportion of each group For instance we display how the main East Coast groups groups 2 9 12 14 15 and 19 are 9 12 14 15 and 19 along the East coast c displays the spatial pattern of groups 8 and 10 In both a and c higher proportion of individual groups is depicted using more opaque i e less transparent colors and different groups are depicted with different colors b d reveals that average June temperature and precipitation gradients seem to strongly constrain the spatial distribution of these breeding bird groups respectively Circles represent the estimated proportion for each location and group while lines depict suitability envelopes These envelopes were created by first defining equally spaced intervals


id: cord-019050-a9datsoo
type: PROTEIN
entity:
  tumor suppressor proteins nuclear transcription factors cell cycle regulatory proteins growth factors


id: cord-131678-rvg1ayp2
type: DNA
entity:
  SIR model synthetic data2 geo loc myLocn plt SIR model synthSIR interactiveFig TRUE sweep SIR models synthetic data2 geo loc MyLocn Listing 12


id: cord-131678-rvg1ayp2
type: DNA
entity:
  repo chr 1 29903 a t t a local chr 1 29903 a t t a 29903 a t t a attr species chr Severe _ acute _ respiratory _ syndrome _ coronavirus _ 2 local List


id: cord-131678-rvg1ayp2
type: DNA
entity:
  1 NC _ 045512 2 chr 1 29903 a t t a attr species chr Severe _ acute _ respiratory _ syndrome _ coronavirus _ 2 ptns List


id: cord-131678-rvg1ayp2
type: DNA
entity:
  2 repo chr 1 117619 YP _ 009742608 YP _ 009742609 YP _ 009742610 YP _ 009742611 local chr 1 117619 YP _ 009742608 YP _ 009742609 YP _ 009742610 YP _ 009742611 chr 1 11142 2020 01 13 T00 00 00 Z 2020 07 17 T00 00 00 Z 2020 07 17 T00 00 00 Z 2020 07 17 T00 00 00 Z chr 1 11142 Severe acute


id: cord-131678-rvg1ayp2
type: DNA
entity:
  coronavirus chr 1 11142 Homo sapiens Homo sapiens Homo sapiens Homo sapiens Isolation _ Source chr 1 11142 Collection _ Date chr 1 11142 2019 12 2020 06 14 2020 06 13 2020 06 13 BioSample chr 1 11142 SAMN15488404 SAMN15488426 SAMN15488426 GenBank _ Title chr 1 11142 Severe acute respiratory syndrome coronavirus 2 isolate Wuhan Hu 1 complete genome


id: cord-131678-rvg1ayp2
type: DNA
entity:
  ERR1857120 ERR1857132 ERR3955908 ERR3955909 sample _ acc chr 1 26776 ERS1572626 ERS1572627 ERS2783634 ERS2783635 biosample chr 1


id: cord-131678-rvg1ayp2
type: PRODUCT
entity:
  CoronaWhy Building a Distributed Credible and Scalable Research and Data Infrastructure for Open Science


id: cord-131678-rvg1ayp2
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entity:
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id: cord-027431-6twmcitu
type: WORK_OF_ART
entity:
  Spatiotemporal Filtering Pipeline for Efficient Social Networks Data Processing Algorithms date 2020


id: cord-032403-9c1xeqg1
type: CELL_LINE
entity:
  Relationship between Cell Culture Process and Monoclonal Antibody Quality Insights into the Dynamics and Control of COVID 19 Infection Rates Early Dynamics of Transmission and Control of COVID 19 A Mathematical Modelling Study Hybrid


id: cord-032403-9c1xeqg1
type: ORG
entity:
  the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use 51


id: cord-032403-9c1xeqg1
type: ORG
entity:
  Transfer Learning Role of Knowledge Management in Development and Lifecycle Management of Biopharmaceuticals Machine Learning Overview


id: cord-032403-9c1xeqg1
type: ORG
entity:
  the Recent Progresses and Implications for the Process Systems Engineering Field Provable Data Integrity


id: cord-032403-9c1xeqg1
type: ORG
entity:
  the Blockchain Organizational Transformation for Sustainable Development A Case Study Management of Permanent Change Digital Finance


id: cord-016448-7imgztwe
type: DISEASE
entity:
  Escherichia coli Epstein Barr virus and virus human protein interaction maps A network based analysis of systemic


id: cord-023284-i0ecxgus
type: PROTEIN
entity:
  ligand Abtstr 236 239 Quant Struct Act Relat 9 234 293 1990 binding unfolding kinetics conformational equilibria


id: cord-023284-i0ecxgus
type: PROTEIN
entity:
  type I 3 phenoxybenzyl 2 4 ethoxyphenyl 3 3 3 trifluoropropyl ether R Quant Struct Act Relat 9 234 293 1990 Abstr 308 271 isomer 11 a cyano 3 phenoxybenzyl 2 4 chlorophenyl 2 methylpropionate S isomer III


id: cord-023284-i0ecxgus
type: PROTEIN
entity:
  I 11 OH 0 Ho2C Quant Struct Act Relat 9 234 293 1990 Abstr 321 322 279 Biological material a Aspergillus ochraceus b Carbopeptidase A


id: cord-023284-i0ecxgus
type: DNA
entity:
  X ray structure d The combination of NMR and molecular modeling approaches however makes it possible to model virtually any molecule


id: cord-023284-i0ecxgus
type: GGP
entity:
  acetylcholine histamine cortisonelhydrocortisone indole 3 acetic acid phenoxyacetic acid herbicides c


id: cord-023284-i0ecxgus
type: GGP
entity:
  isosorbide l 1 5 monoacetate 1 2 monoacetate 1 5 mononitrate 1 2 mononitrate l diacetate 1 5 nitro 2 acetate 1 2 nitro 5 acetate l dinitrate Rn R I retention factors


id: cord-023284-i0ecxgus
type: CHEMICAL
entity:
  methyl 2 nitropheno1 2 4dichlorophenol 2 4 6 trichlorophenol 2 3 4 5 tetrachlorophenol 2 4 6 triiodophenol pentachlorophenol


id: cord-023284-i0ecxgus
type: CHEMICAL
entity:
  3 6a dihydroxy 8 9 methylenedioxy pterocarpan 6a 1 la dehydropisatin 3 hydroxy 8 9 methylenedioxy 6a 1 1 a dehydropterocarpan 3 hydroxy 9 methoxypterocarpan 3 hydroxy 9 zmethoxypterocarpan 3 zhydroxy 9 methoxypterocarpan vestitol sativan


id: cord-023284-i0ecxgus
type: CHEMICAL
entity:
  2 C1 3 C1 2 4 5 Cl 2 4 6 CI pentachlorophenol 4 CI 2 Me 4 C1 3 Me 4 C1 2 3 Me 4 C1 3 5 Me 4 CI 2 ally1 4 C1 2 Pr 5 Me 4Z1 2 3 C1 2 443 2 5 C1 2 6 Cl 3 4 C1 3 5 CI 2 3 6 C1 2 Cl 6 NO2 2 4 C1


id: cord-023284-i0ecxgus
type: CHEMICAL
entity:
  N substituted 3 methyl 4 nitropyrazole 5 carboxamides 11 6 N substituted 4 amino 3 methylpyrazole 5 carboxamides


id: cord-023284-i0ecxgus
type: CHEMICAL
entity:
  N substituted 3 methyl 4 diazopyrazole 5 carboxamides and N piperidiny 1 N 1 3 dimethyl 4 nitrosopyrazol 5 yl urea


id: cord-027712-2o4svbms
type: WORK_OF_ART
entity:
  Baseline Modelling and Composite Representation of Unobtrusively IoT Sensed Behaviour Changes Related to Urban


id: cord-032383-2dqpxumn
type: GGP
entity:
  anosmia 26 and ageusia 24 reported on Twitter were not found in the clinical studies A generic workflow of ML and NLP application on social media data is illustrated in Fig 3 Several articles have performed bibliometric research on scientific works focused on COVID 19 The Allen Institute for AI with other collaborators started an initiative for collecting articles on COVID 19 research named CORD 19 6 The data set was initiated with 28K articles now contains more than 52k articles and 41k full texts Multiple repositories such as PMC BioRxiv MedRxiv and WHO were searched with queries related to COVID 19 COVID 19 Coronavirus Corona virus 2019 nCoV


id: cord-002774-tpqsjjet
type: GGP
entity:
  Ii 0 11 p 0 01 and symptoms of pain Ii 0 14 p 0 01 fatigue ji 0 34 p 0 001 and cognitive difficulties p 0 30 P 0 001 For the three dimensions of the IIRS multiple regression results revealed 1


id: cord-002774-tpqsjjet
type: LAW
entity:
  The Collaborative Process to Achieve Access to Primary Health Care for Black Women and Women of Colour Project


id: cord-002774-tpqsjjet
type: EVENT
entity:
  Black Women and Women of Colour Women s Health in Women s Hands Community Health Centre Sistering Parkdale Community Health Centre Rexdale Community Health Centre


id: cord-002774-tpqsjjet
type: LAW
entity:
  P5 77 C Health and Livelihood Implications of Marginalization of Slum Dwellers in Provision of Water and Sanitation Services


id: cord-002774-tpqsjjet
type: DISEASE
entity:
  HIV Criminal justice system involvement was measured by arrest incarceration drug felony and loss denial of social services


id: cord-032607-bn8g02gi
type: LAW
entity:
  the Findability Accessibility Interoperability and Reusability FAIR and Care and Five Safes Principles We


id: cord-032607-bn8g02gi
type: ORG
entity:
  CONSORT Extension for Trials Using Cohorts and Routinely Collected Health Data SPIRIT and Template for Intervention Description and Replication


id: cord-004894-75w35fkd
type: CELL_LINE
entity:
  APC CSYMBOL colon tumours RR 0 58 95 CI 0 32 1 05 for the highest versus the lowest tertile of folate intake but positively associated with APC


id: cord-004894-75w35fkd
type: PROTEIN
entity:
  amino terminal pro B type natriuretic peptide ROC area 0 80 or electrocardiography ROC area 0 77 A CMR model


id: cord-004894-75w35fkd
type: ORG
entity:
  Disease High Risk Populations Growth and Development Public Health Strategies Translational Epidemiology Sessions


id: cord-004894-75w35fkd
type: ORG
entity:
  Short Portable Mental State Questionnaire Geriatric Depression Scale Activities of Daily Living ADL Instrumental Activities of Daily Living


id: cord-102490-yvcrv94c
type: ORG
entity:
  Artificial Intelligence Applied to Software Paraconsistent Measurement Software Post Undergraduate Degree


id: cord-102490-yvcrv94c
type: ORG
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  Information Technology POST GRADUATE Teaching for Higher Education FALC Faculty Aldeia de Carapicuíba POST


id: cord-133273-kvyzuayp
type: ORG
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  Keywords Computational Materials Science Property Structure Mapping Texture Evolution Optimization Machine Learning Reinforcement Learning


id: cord-133273-kvyzuayp
type: ORG
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  Medicine DICOM Standard National Electrical Manufacturers Association CT Realistic Lung Nodule Simulation


id: cord-133273-kvyzuayp
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  Past Present and Future A Survey on Specialised Hardware for Machine Learning A Survey on Distributed Machine Learning Hardware


id: cord-133273-kvyzuayp
type: ORG
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  Wearable Stress and Affect Detection Feasibility and Usability Aspects of Continuous Remote Monitoring of Health Status


id: cord-133273-kvyzuayp
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  Towards International Standards for the Evaluation of Artificial Intelligence for Health Proposed Regulatory Framework for Modifications to Artificial Intelligence Machine Learning


id: cord-133273-kvyzuayp
type: WORK_OF_ART
entity:
  A Comparative Analysis of Cross Correlation Matching Algorithms Using a Pyramidal Resolution Approach Fast


id: cord-133273-kvyzuayp
type: WORK_OF_ART
entity:
  A Large Dataset to Train Convolutional Networks for Disparity Optical Flow and Scene Flow Estimation Pyramid Stereo Matching Network Microstructure


id: cord-209932-1lsv7cel
type: DISEASE
entity:
  Kidney stone Leukemia Liver tumour Lung cancer Malaria Melena Memory Loss Menopause Mesothelioma Migraine Miscarriage Mucus


id: cord-176677-exej3zwh
type: FAC
entity:
  Paper Mâché Creating Dynamic Reproducible Science A Provenance Based Infrastructure to Support the Life Cycle of Executable Papers A Universal Identifier for Computational


id: cord-035030-ig4nwtmi
type: FAC
entity:
  the Charité Berlin Hannover Medical School MHH University of Düsseldorf and Institute of Human Genetics


id: cord-035030-ig4nwtmi
type: EVENT
entity:
  New Strategies for the Conduct of Clinical Trials in Pediatric Pulmonary Arterial Hypertension Outcome


id: cord-035030-ig4nwtmi
type: DISEASE
entity:
  congenital malformations Malformations of the gastrointestinal tract the abdominal wall the diaphragm


id: cord-184194-zdxebonv
type: ORG
entity:
  America Public Opinion Poll Report Summary 2019 National Health Insurance Administration Ministry of Health and Welfare Statistics


id: cord-259247-7loab74f
type: ORG
entity:
  The Economist Big Data A Revolution That Will Transform How we Live Work and Think The Triumph of Injustice


id: cord-198180-pwmr3m4o
type: ORG
entity:
  a Systems Architecture to Improve Smart Cities Technology Approaches Research on Key Technology for Data Storage


id: cord-198180-pwmr3m4o
type: ORG
entity:
  Predictive Active Steering Control for Autonomous Vehicle Systems Collision Avoidance and Stabilization for Autonomous Vehicles


id: cord-160526-27kmder5
type: ORG
entity:
  methods European Medicines Agency Committee For Medicinal Products For Human Use EMA CHMP Guidance to Sponsors


id: cord-160526-27kmder5
type: ORG
entity:
  Missing Data A General Statistical Principle for Changing a Design Any Time During the Course of a Trial Robust Inference


id: cord-226263-ns628u21
type: WORK_OF_ART
entity:
  Enhancing Robustness of Android Malware Detection System against Adversarial Attacks on Heterogeneous Graph


id: cord-146850-5x6qs2i4
type: ORG
entity:
  s Secret Auto Insurance Algorithm Squeezes Big Spenders Algorithmic Injustices Towards a Relational Ethics Social Biases


id: cord-102238-g6dsnhmm
type: WORK_OF_ART
entity:
  An Overview on Vibration Analysis Techniques for the Diagnosis of Rolling Element Bearing Faults Pattern Recognition Statistical Structural


id: cord-183016-ajwnihk6
type: EVENT
entity:
  the Global Public Health Intelligence Network GPHIN 10 and the Global Outbreak Alert and Response Network


id: cord-169484-mjtlhh5e
type: WORK_OF_ART
entity:
  A Language and Environment for Statistical Computing R Foundation for Statistical Computing Regierungsprotokoll


id: cord-204835-1yay69kq
type: DNA
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  GRU D 1 0 829 0 003 0 818 0 009 0 835 0 013 0 965 0 003 M RNN 49 0 827 0 005 0 820 0 011 0 842 0 010 0 959 0 003 Brits 34 0 833 0 002 0 819 0 012 0 839 0 013 0 959 0 002 T LSTM 17 0 817 0 004 0 804 0 010 0 831 0 014 0 963 0 003 DATA GRU 36 0 832 0 006 0 822 0 012 0 851 0 012 0 961 0 003 LGnet 32 0 833 0 003 0 822 0 013 0 843 0 013 0 956 0 002 IPN 38 0 831 0 003 0 824 0 009 0 844 0 015 0 960 0 003


id: cord-238342-ecuex64m
type: ORG
entity:
  Composite Monte Carlo Decision Making under High Uncertainty of Novel Coronavirus Epidemic Using Hybridized Deep Learning


id: cord-238342-ecuex64m
type: ORG
entity:
  Stochastic Evacuation Models for Decision Support in Actual Emergencies On Algorithmic Decision Procedures in Emergency Response Systems


id: cord-267485-1fu1blu0
type: ORG
entity:
  National Bioterrorism Syndromic Surveillance Demonstration Program US Department of Health Human Services Health Insurance Portability and Accountability Act


id: cord-267485-1fu1blu0
type: ORG
entity:
  the Centers for Disease Control and Prevention Massachusetts Department of Public Health Public Cooperative Agreement for Health Preparedness and Response for Bioterrorism


id: cord-223332-51670qld
type: ORG
entity:
  the Council of European Union The Personal Data Protection Bill Social Security Number Protection Act


id: cord-223332-51670qld
type: WORK_OF_ART
entity:
  Large Sparse Datasets Security Without Identification Transaction Systems to Make Big Brother Obsolete Showing Credentials Without Identification


id: cord-223332-51670qld
type: WORK_OF_ART
entity:
  Credential Sharing A Pitfall of Anonymous Credentials Untraceable Electronic Mail Return Addresses and Digital Pseudonyms


id: cord-223332-51670qld
type: WORK_OF_ART
entity:
  Towards Defining Semantic Foundations for Purpose Based Privacy Policies Formalizing and Enforcing Purpose Restrictions in Privacy Policies


id: cord-223332-51670qld
type: ORG
entity:
  Order Execution Sanctum Minimal Hardware Extensions for Strong Software Isolation Practical Techniques for Searches on Encrypted Data Public Key Encryption


id: cord-223332-51670qld
type: ORG
entity:
  India Apps Gone Rogue Maintaining Personal Privacy in an Epidemic Anonymous Collocation Discovery Harnessing Privacy to Tame the Coronavirus Epione Lightweight Contact Tracing with Strong


id: cord-272276-83f0ruku
type: DNA
entity:
  Veterinary Medical 12 13 14 15 16 17 18 19 20 21 22 23 24 ll l111lll llLllll1111111111111111 1111 Diagnostic Laboratory demographic and


id: cord-269693-9tsy79lt
type: ORG
entity:
  Enterprise Information Systems Journal of Global Information Management and International Journal of Environmental Research and


id: cord-269693-9tsy79lt
type: WORK_OF_ART
entity:
  Investigating the Relationship between Industry 4 0 and Productivity a Conceptual Framework for Malaysian Manufacturing Firms Exploring


id: cord-270721-81axdn0g
type: PROTEIN
entity:
  Coronavirus COVID 19 in China and Will Influence Future Urban Health Policy Internationally The Potential of Blockchain


id: cord-270721-81axdn0g
type: WORK_OF_ART
entity:
  The Emergence of Voluntary Citizen Networks to Circumvent Urban Health Data Sharing Restrictions During Pandemics date 2020


id: cord-270721-81axdn0g
type: ORG
entity:
  Low Middle Income Country Scientists Data Privacy and Ethical Uses of Volunteered Geographic Information


id: cord-274019-dao10kx9
type: WORK_OF_ART
entity:
  The Confounding Effect of Population Structure on Bayesian Skyline Plot Inferences of Demographic History Bayesian


id: cord-274019-dao10kx9
type: ORG
entity:
  Posterior Distributions for a Star Tree IQ TREE A Fast and Effective Stochastic Algorithm for Estimating Maximum Likelihood Phylogenies


id: cord-275742-7jxt6diq
type: ORG
entity:
  the Florida State University Library Services The Engineering of Knowledge Based Systems Theory and Practice


id: cord-275742-7jxt6diq
type: WORK_OF_ART
entity:
  Making the Case for Artificial Intelligence at Government Guidelines to Transforming Federal Software Systems Data Democracy At the Nexus of Artificial Intelligence Software Development and Knowledge Engineering Evidence


id: cord-286288-gduhterq
type: ORG
entity:
  the International Conference of Harmonization the International Organization for Standardization and the International Medical Device Regulators Forum


id: cord-022633-fr55uod6
type: PROTEIN
entity:
  interleukin 6 IL 6 C reactive protein CRP tumor necrosis factor alpha TNF alpha vascular cell adhesion molecule VCAM E selectin lipoprotein


id: cord-022633-fr55uod6
type: GGP
entity:
  kappa 0 85 p 0 001 Conclusion Central and local adjudication resulted in significantly different rates of AMI and ACS


id: cord-022633-fr55uod6
type: GGP
entity:
  MAP p 0 01 but not in HR p 0 34 There was a significant difference p 0 023 in time to peak MAP in the ILE


id: cord-022633-fr55uod6
type: GGP
entity:
  Hospital 1 1 01 12 31 06 Brigham and Women s Hospital 1 1 01 12 31 06 Children s Hospital Boston 1 1 01 12 31 06 Hasbro Children s Hospital 1 1 04 12 31 09 We reviewed the medical records of all


id: cord-022633-fr55uod6
type: ORG
entity:
  the University of Pennsylvania Philadelphia PA 2 Cooper University Hospital Camden NJ Background Patients


id: cord-022633-fr55uod6
type: WORK_OF_ART
entity:
  A Randomized Controlled Feasibility Trial of Vacant Lot Greening to Reduce Crime and Increase Perceptions of Safety Eugenia C


id: cord-022633-fr55uod6
type: WORK_OF_ART
entity:
  Screening for Violence Identifies Young Adults at Risk for Return ED Visits for Injury Abigail Hankin Wei Brittany Meagley Debra Houry Emory University Atlanta GA Background Homicide


id: cord-022633-fr55uod6
type: ORG
entity:
  Impact of Emergency Physician Board Certification on Patient Perceptions of ED Care Quality Albert G Sledge


id: cord-022633-fr55uod6
type: WORK_OF_ART
entity:
  The Use of Ultrasound to Evaluate Traumatic Optic Neuropathy Benjamin Burt Lisa Montgomery Cynthia Garza Meissner Sanja


id: cord-022633-fr55uod6
type: WORK_OF_ART
entity:
  Adverse Effects Following Prehospital Use Of Ketamine By Paramedics Eric Ardeel Baylor College of Medicine


id: cord-022633-fr55uod6
type: DISEASE
entity:
  cardiopulmonary adverse event RR 1 08 95 CI 0 68 1 69 within 30 days The table tabulates interventions


id: cord-291975-y8ck4lo8
type: ORG
entity:
  The United Nations Probabilistic Population Projections An Introduction to Demographic Forecasting with


id: cord-295013-ew9n9i7z
type: ORG
entity:
  Geneva World Health Organization Primary Health Care on the Road to Universal Health Coverage Monitoring


id: cord-295013-ew9n9i7z
type: ORG
entity:
  India Ministry of Health and Family Welfare Ayushman Bharat National Health Protection Mission New Delhi


id: cord-289447-d93qwjui
type: FAC
entity:
  the Quantitative Proteome of Kinase Knockouts Understanding Machine Learning From Theory to Algorithms Combining Machine Learning and Metabolomics


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entity:
  A Mobile Cough Strength Evaluation Device Using Cough Sounds Heart Rate Monitoring System During Physical Exercise for Fatigue Warning


id: cord-292475-jrl1fowa
type: PERSON
entity:
  ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi


id: cord-292475-jrl1fowa
type: ORG
entity:
  Image Deconvolution and Reconstruction Different Epidemic Curves for Severe Acute Respiratory Syndrome


id: cord-297811-8gyejoc5
type: ORG
entity:
  The JavaScript Object Notation JSON Data Interchange Format RFC 7159 GeoJSON Specification Middle East


id: cord-315531-2gc2dc46
type: ORG
entity:
  Systems Integration of Biodefense Omics Data for Analysis of Pathogen Host Interactions and Identification of Potential Targets


id: cord-317853-vd35a2eq
type: ORG
entity:
  the World Organisation for Animal Health OIE Food and Agriculture Organization of the United Nations


id: cord-320040-h8v6cs5b
type: ORG
entity:
  Washington Institute of Medicine National Academies Press Data Sharing Common Data Element CDE Resource Portal


id: cord-320040-h8v6cs5b
type: ORG
entity:
  National Institutes of Health An R D Blueprint for Action to Prevent Epidemics Geneva World Health Organization


id: cord-324198-b8f99z8r
type: PROTEIN
entity:
  Coronavirus COVID 19 in China and Will Influence Future Urban Health Policy Internationally Climate Change and Economic Resilience through Urban and Cultural Heritage The Case of Emerging Small Island Developing States Economies


id: cord-327810-kquh59ry
type: WORK_OF_ART
entity:
  Understanding the Actualization of Affordances A Study in the Process Modeling Context Bounded Semiotics


id: cord-327810-kquh59ry
type: WORK_OF_ART
entity:
  Confronting the Risks of Artificial Intelligence Share of People using Internet Banking in Great Britain New Games


id: cord-328438-irjo0l4s
type: CELL_LINE
entity:
  LEADLESS trial Symbiotic cardiac pacemaker First experience with a mobile cardiac outpatient telemetry MCOT system for the diagnosis and management of cardiac arrhythmia The diagnosis of cardiac arrhythmias a prospective multi center randomized study comparing mobile cardiac outpatient telemetry versus standard loop event monitoring Worldwide epidemiology of atrial fibrillation a global burden of disease 2010 study Comparison of 2 ambulatory patch ECG monitors the benefit of the P wave and signal clarity Wearable real time heart attack detection and warning system to reduce road accidents A comparative study of ECG derived respiration in ambulatory monitoring using the single lead ECG Real world performance of an enhanced atrial fibrillation


id: cord-328438-irjo0l4s
type: ORG
entity:
  the American College of Cardiology American Heart Association Task Force on Clinical Practice Guidelines


id: cord-339440-qu913a8q
type: WORK_OF_ART
entity:
  Comput Simulating the Potential Impacts of COVID 19 School Closures on Schooling and Learning Outcomes


id: cord-339440-qu913a8q
type: ORG
entity:
  García Peñalvo FJ RoboSTEAM Project Systematic Mapping Challenge Based Learning and Robotics Robotics


id: cord-338207-60vrlrim
type: ORG
entity:
  The Gene Ontology Consortium National Center for Biotechnology Information Viral Genomes Project Virus Taxonomy Classification


id: cord-339491-lyld3up2
type: FAC
entity:
  the New York City Area Association of Public Health Interventions With the Epidemiology of the COVID 19 Outbreak


id: cord-339491-lyld3up2
type: ORG
entity:
  the K means Algorithm for Clustering Large Data Sets with Categorical Value Silhouettes A Graphical Aid


id: cord-347199-slq70aou
type: ORG
entity:
  the U S Department of Energy Office of Science Office of Advanced Scientific Computing Research Scientific


id: cord-346309-hveuq2x9
type: ORG
entity:
  the Centre for Epidemiology and Research New South Wales Department of Health New South Wales Emergency Department Data Collection


id: cord-346309-hveuq2x9
type: ORG
entity:
  Centers for Disease Control and Prevention called Framework for Evaluating Public Health Surveillance Systems


id: cord-339886-th1da1bb
type: ORG
entity:
  Committee on Achieving Sustainable Global Capacity for Surveillance and Response to Emerging Diseases of Zoonotic Origin


id: cord-349790-dezauioa
type: ORG
entity:
  Office of Advanced Molecular Detection National Center for Emerging and Zoonotic Infectious Diseases Centers for Disease Control and Prevention Atlanta


# url

id: cord-103813-w2sb6h94
domain: www.cisa.gov
url:
  http://www.cisa.gov/news/2020/05/13/fbi-and-cisa-warn-against-chinese-targeting-covid-19-research-organizations


id: cord-198180-pwmr3m4o
domain: www.forbes.com
url:
  http://www.forbes.com/sites/louiscolumbus/2018/08/16/iot-market-predicted-to-double-by-2021-reaching-520b/#82674f91f948


id: cord-198180-pwmr3m4o
domain: www.cisco.com
url:
  http://www.cisco.com/c/dam/en/us/products/collateral/se/internet-of-things/at-a-glance-c45-731471.pdf


id: cord-198180-pwmr3m4o
domain: www.fda.gov
url:
  http://www.fda.gov/drugs/drug-safety-and-availability/fda-cautions-against-use-hydroxychloroquine-or-chloroquine-COVID-19-outside-hospital-setting-or


id: cord-198180-pwmr3m4o
domain: talkbusiness.net
url:
  http://talkbusiness.net/2020/04/groups-share-data-quantifying-COVID-19-impacts-on-trucking-industry/


id: cord-198180-pwmr3m4o
domain: spectrum.ieee.org
url:
  http://spectrum.ieee.org/news-from-around-ieee/the-institute/ieee-member-news/thermal-cameras-are-beingoutfitted-to-detect-fever-and-conduct-contact-tracing-for-covid19


id: cord-198180-pwmr3m4o
domain: www.fiercewireless.com
url:
  http://www.fiercewireless.com/iot/at-t-4g-lte-connects-iot-robots-to-kill-germs-keep-shelves-stocked


id: cord-198180-pwmr3m4o
domain: spectrum.ieee.org
url:
  http://spectrum.ieee.org/telecom/security/tracking-covid19-with-the-iot-may-put-your-privacy-at-risk


id: cord-198180-pwmr3m4o
domain: www.govtech.com
url:
  http://www.govtech.com/opinion/If-Only-One-US-City-Wins-the-Smart-City-Race-the-Whole-Nation-Loses.html


id: cord-169484-mjtlhh5e
domain: www.derstandard.at
url:
  http://www.derstandard.at/jetzt/livebericht/2000088169126/buwog-prozessvermoegensverwalter-stinksauer-auf-meischberger


# bib

id: cord-001470-hn288o97
author: Pivette, Mathilde
date: 2014-11-18
txt: ./txt/cord-001470-hn288o97.txt
cache: ./cache/cord-001470-hn288o97.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4240820/
doi: 10.1186/s12879-014-0604-2
summary:
  CONCLUSIONS: Drug sales data analyses appear to be a useful tool for surveillance of gastrointestinal and respiratory disease, and OTC drugs have the potential for early outbreak detection. Published articles were searched for on electronic databases (Pubmed, Embase, Scopus, LILACS, African Index Medicus, Cochrane Library), using combinations of the following key words: ("surveillance" OR outbreak detection OR warning system) AND (overthe-counter OR "prescription drugs" OR pharmacy OR (pharmaceutical OR drug OR medication) sales). Articles excluded based on fulltext review (no drug sales data, no infectious disease, no outbreak detection) N= 85 Figure 1 Flow chart of study selection process in a systematic review of drug sales data analysis for syndromic surveillance of infectious diseases. Nineteen of the 27 studies were descriptive retrospective studies assessing the strength of the correlation between drug sales and reference surveillance data of the corresponding disease or evaluating outbreak-detection performance [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] .

title:
  Drug sales data analysis for outbreak detection of infectious diseases: a systematic literature review

abstract:
  BACKGROUND: This systematic literature review aimed to summarize evidence for the added value of drug sales data analysis for the surveillance of infectious diseases. METHODS: A search for relevant publications was conducted in Pubmed, Embase, Scopus, Cochrane Library, African Index Medicus and Lilacs databases. Retrieved studies were evaluated in terms of objectives, diseases studied, data sources, methodologies and performance for real-time surveillance. Most studies compared drug sales data to reference surveillance data using correlation measurements or indicators of outbreak detection performance (sensitivity, specificity, timeliness of the detection). RESULTS: We screened 3266 articles and included 27 in the review. Most studies focused on acute respiratory and gastroenteritis infections. Nineteen studies retrospectively compared drug sales data to reference clinical data, and significant correlations were observed in 17 of them. Four studies found that over-the-counter drug sales preceded clinical data in terms of incidence increase. Five studies developed and evaluated statistical algorithms for selecting drug groups to monitor specific diseases. Another three studies developed models to predict incidence increase from drug sales. CONCLUSIONS: Drug sales data analyses appear to be a useful tool for surveillance of gastrointestinal and respiratory disease, and OTC drugs have the potential for early outbreak detection. Their utility remains to be investigated for other diseases, in particular those poorly surveyed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12879-014-0604-2) contains supplementary material, which is available to authorized users.


id: cord-002366-t94aufs3
title: EuPathDB: the eukaryotic pathogen genomics database resource
author: Aurrecoechea, Cristina
date: 2017-01-04
txt: ./txt/cord-002366-t94aufs3.txt
cache: ./cache/cord-002366-t94aufs3.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5210576/
doi: 10.1093/nar/gkw1105
summary:
  To facilitate the discovery of meaningful biological relationships, the databases couple preconfigured searches with visualization and analysis tools for comprehensive data mining via intuitive graphical interfaces and APIs. All data are analyzed with the same workflows, including creation of gene orthology profiles, so data are easily compared across data sets, data types and organisms. Expanded data content is mostly genomic and functional genomic data while new data types include protein microarray, metabolic pathways, compounds, quantitative proteomics, copy number variation, and polysomal transcriptomics. New features include consistent categorization of searches, data sets and genome browser tracks; redesigned gene pages; effective integration of alternative transcripts; and a EuPathDB Galaxy instance for private analyses of a user''s data. The near-seamless integration of strategy results with tools for functional enrichment analyses and transcript interpretation as well as our new Galaxy workspace and the availability of publicly shared strategies augment the data mining experience in EuPathDB.

abstract:
  The Eukaryotic Pathogen Genomics Database Resource (EuPathDB, http://eupathdb.org) is a collection of databases covering 170+ eukaryotic pathogens (protists & fungi), along with relevant free-living and non-pathogenic species, and select pathogen hosts. To facilitate the discovery of meaningful biological relationships, the databases couple preconfigured searches with visualization and analysis tools for comprehensive data mining via intuitive graphical interfaces and APIs. All data are analyzed with the same workflows, including creation of gene orthology profiles, so data are easily compared across data sets, data types and organisms. EuPathDB is updated with numerous new analysis tools, features, data sets and data types. New tools include GO, metabolic pathway and word enrichment analyses plus an online workspace for analysis of personal, non-public, large-scale data. Expanded data content is mostly genomic and functional genomic data while new data types include protein microarray, metabolic pathways, compounds, quantitative proteomics, copy number variation, and polysomal transcriptomics. New features include consistent categorization of searches, data sets and genome browser tracks; redesigned gene pages; effective integration of alternative transcripts; and a EuPathDB Galaxy instance for private analyses of a user's data. Forthcoming upgrades include user workspaces for private integration of data with existing EuPathDB data and improved integration and presentation of host–pathogen interactions.


id: cord-002774-tpqsjjet
title: Section II: Poster Sessions
author: nan
date: 2017-12-01
txt: ./txt/cord-002774-tpqsjjet.txt
cache: ./cache/cord-002774-tpqsjjet.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5711696/
doi: 10.1093/jurban/jti137
abstract: nan
summary:
  Results: The CHIP Framework The CHIP framework aims to improve the health and wellness of the urban communities served by St. Josephs Health Centre through four intersecting pillars: • Raising Community Voices provides an infrastructure and process that supports community stakeholder input into health care service planning, decision-making, and delivery by the hospital and across the continuum of care; • Sharing Reciprocal Capacity promotes healthy communities through the sharing of our intellectual and physical capacity with our community partners; • Cultivating Integration Initiatives facilitates vertical, horizontal, and intersectoral integration initiatives in support of community-identified needs and gaps; and • Facilitating Healthy Exchange develops best practices in community integration through community-based research, and facilitates community voice in informing public policy.


id: cord-003243-u744apzw
author: Michael, Edwin
date: 2018-10-08
txt: ./txt/cord-003243-u744apzw.txt
cache: ./cache/cord-003243-u744apzw.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6175292/
doi: 10.1371/journal.pntd.0006674
summary:
  METHODOLOGY AND PRINCIPAL FINDINGS: We report on the development of an analytical framework to quantify the relative values of various longitudinal infection surveillance data collected in field sites undergoing mass drug administrations (MDAs) for calibrating three lymphatic filariasis (LF) models (EPIFIL, LYMFASIM, and TRANSFIL), and for improving their predictions of the required durations of drug interventions to achieve parasite elimination in endemic populations. We report on the development of an analytical framework to quantify the relative values of various longitudinal infection surveillance data collected in field sites undergoing mass drug administrations (MDAs) for calibrating three lymphatic filariasis (LF) models (EPIFIL, LYM-FASIM, and TRANSFIL), and for improving their predictions of the required durations of drug interventions to achieve parasite elimination in endemic populations.

title:
  Quantifying the value of surveillance data for improving model predictions of lymphatic filariasis elimination

abstract:
  BACKGROUND: Mathematical models are increasingly being used to evaluate strategies aiming to achieve the control or elimination of parasitic diseases. Recently, owing to growing realization that process-oriented models are useful for ecological forecasts only if the biological processes are well defined, attention has focused on data assimilation as a means to improve the predictive performance of these models. METHODOLOGY AND PRINCIPAL FINDINGS: We report on the development of an analytical framework to quantify the relative values of various longitudinal infection surveillance data collected in field sites undergoing mass drug administrations (MDAs) for calibrating three lymphatic filariasis (LF) models (EPIFIL, LYMFASIM, and TRANSFIL), and for improving their predictions of the required durations of drug interventions to achieve parasite elimination in endemic populations. The relative information contribution of site-specific data collected at the time points proposed by the WHO monitoring framework was evaluated using model-data updating procedures, and via calculations of the Shannon information index and weighted variances from the probability distributions of the estimated timelines to parasite extinction made by each model. Results show that data-informed models provided more precise forecasts of elimination timelines in each site compared to model-only simulations. Data streams that included year 5 post-MDA microfilariae (mf) survey data, however, reduced each model’s uncertainty most compared to data streams containing only baseline and/or post-MDA 3 or longer-term mf survey data irrespective of MDA coverage, suggesting that data up to this monitoring point may be optimal for informing the present LF models. We show that the improvements observed in the predictive performance of the best data-informed models may be a function of temporal changes in inter-parameter interactions. Such best data-informed models may also produce more accurate predictions of the durations of drug interventions required to achieve parasite elimination. SIGNIFICANCE: Knowledge of relative information contributions of model only versus data-informed models is valuable for improving the usefulness of LF model predictions in management decision making, learning system dynamics, and for supporting the design of parasite monitoring programmes. The present results further pinpoint the crucial need for longitudinal infection surveillance data for enhancing the precision and accuracy of model predictions of the intervention durations required to achieve parasite elimination in an endemic location.


id: cord-004464-nml9kqiu
author: Lhommet, Claire
date: 2020-03-06
txt: ./txt/cord-004464-nml9kqiu.txt
cache: ./cache/cord-004464-nml9kqiu.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7060632/
doi: 10.1186/s12890-020-1089-y
summary:
  title: Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation? Whether the etiology of CAP is viral or bacterial should be determined based on the patient interview, clinical symptoms and signs, biological findings and radiological data from the very first hours of the patient''s presentation (a time when microbiological findings are typically not yet available). The aim of our study was to evaluate and compare the abilities of experienced physicians and a data-driven approach to answer this simple question within the first hours of a patient''s admission to the ICU for CAP: is it a viral or a bacterial pneumonia? Step 2: clinician and data-driven predictions of microbial etiology Clinicians and a mathematical algorithm were tasked with predicting the microbial etiology of pneumonia cases based on all clinical (43 items), and biological or radiological (17 items) information available in the first 3-h period after admission except for any microbiological findings (Supplementary Table 1 ).

title:
  Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

abstract:
  BACKGROUND: Community-acquired pneumonia (CAP) requires urgent and specific antimicrobial therapy. However, the causal pathogen is typically unknown at the point when anti-infective therapeutics must be initiated. Physicians synthesize information from diverse data streams to make appropriate decisions. Artificial intelligence (AI) excels at finding complex relationships in large volumes of data. We aimed to evaluate the abilities of experienced physicians and AI to answer this question at patient admission: is it a viral or a bacterial pneumonia? METHODS: We included patients hospitalized for CAP and recorded all data available in the first 3-h period of care (clinical, biological and radiological information). For this proof-of-concept investigation, we decided to study only CAP caused by a singular and identified pathogen. We built a machine learning model prediction using all collected data. Finally, an independent validation set of samples was used to test the pathogen prediction performance of: (i) a panel of three experts and (ii) the AI algorithm. Both were blinded regarding the final microbial diagnosis. Positive likelihood ratio (LR) values > 10 and negative LR values < 0.1 were considered clinically relevant. RESULTS: We included 153 patients with CAP (70.6% men; 62 [51–73] years old; mean SAPSII, 37 [27–47]), 37% had viral pneumonia, 24% had bacterial pneumonia, 20% had a co-infection and 19% had no identified respiratory pathogen. We performed the analysis on 93 patients as co-pathogen and no-pathogen cases were excluded. The discriminant abilities of the AI approach were low to moderate (LR+ = 2.12 for viral and 6.29 for bacterial pneumonia), and the discriminant abilities of the experts were very low to low (LR+ = 3.81 for viral and 1.89 for bacterial pneumonia). CONCLUSION: Neither experts nor an AI algorithm can predict the microbial etiology of CAP within the first hours of hospitalization when there is an urgent need to define the anti-infective therapeutic strategy.


id: cord-004647-0fuy5tlp
author: Patson, Noel
date: 2020-03-20
txt: ./txt/cord-004647-0fuy5tlp.txt
cache: ./cache/cord-004647-0fuy5tlp.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085184/
doi: 10.1186/s12936-020-03190-z
summary:
  METHODS: The search included five databases (PubMed, Embase, Scopus, Malaria in Pregnancy Library and Cochrane Central Register of Controlled Trials) to identify original English articles reporting Phase III randomized controlled trials (RCTs) on anti-malarial drugs for malaria prevention in pregnancy published from January 2010 to July 2019. This review, therefore, aims at identifying applied statistical methods and their appropriateness in the analysis of safety data in anti-malarial drugs for malaria prevention during pregnancy clinical trials. This review sought to provide a detailed overview of the actual practice of the statistical analysis of safety data in the unique setting of drug trials for the preventions of malaria in pregnancy as reflected published literature. Advantageously, methods based on causal inference framework, such as mediation analysis [28] [29] [30] [31] could be adapted/extended to assess the influence of the AEs on non-adherence in RCTs. Despite about three-quarters of the trials reporting p-values after comparing safety outcomes by treatment arms, only about half of the reviewed trials adhered to International Harmonisation Conference Guideline E9 in reporting of confidence intervals in quantifying the safety effect size [3, 4] .

title:
  Systematic review of statistical methods for safety data in malaria chemoprevention in pregnancy trials

abstract:
  BACKGROUND: Drug safety assessments in clinical trials present unique analytical challenges. Some of these include adjusting for individual follow-up time, repeated measurements of multiple outcomes and missing data among others. Furthermore, pre-specifying appropriate analysis becomes difficult as some safety endpoints are unexpected. Although existing guidelines such as CONSORT encourage thorough reporting of adverse events (AEs) in clinical trials, they provide limited details for safety data analysis. The limited guidelines may influence suboptimal analysis by failing to account for some analysis challenges above. A typical example where such challenges exist are trials of anti-malarial drugs for malaria prevention during pregnancy. Lack of proper standardized evaluation of the safety of antimalarial drugs has limited the ability to draw conclusions about safety. Therefore, a systematic review was conducted to establish the current practice in statistical analysis for preventive antimalarial drug safety in pregnancy. METHODS: The search included five databases (PubMed, Embase, Scopus, Malaria in Pregnancy Library and Cochrane Central Register of Controlled Trials) to identify original English articles reporting Phase III randomized controlled trials (RCTs) on anti-malarial drugs for malaria prevention in pregnancy published from January 2010 to July 2019. RESULTS: Eighteen trials were included in this review that collected multiple longitudinal safety outcomes including AEs. Statistical analysis and reporting of the safety outcomes in all the trials used descriptive statistics; proportions/counts (n = 18, 100%) and mean/median (n = 2, 11.1%). Results presentation included tabular (n = 16, 88.9%) and text description (n = 2, 11.1%). Univariate inferential methods were reported in most trials (n = 16, 88.9%); including Chi square/Fisher’s exact test (n = 12, 66.7%), t test (n = 2, 11.1%) and Mann–Whitney/Wilcoxon test (n = 1, 5.6%). Multivariable methods, including Poisson and negative binomial were reported in few trials (n = 3, 16.7%). Assessment of a potential link between missing efficacy data and safety outcomes was not reported in any of the trials that reported efficacy missing data (n = 7, 38.9%). CONCLUSION: The review demonstrated that statistical analysis of safety data in anti-malarial drugs for malarial chemoprevention in pregnancy RCTs is inadequate. The analyses insufficiently account for multiple safety outcomes potential dependence, follow-up time and informative missing data which can compromise anti-malarial drug safety evidence development, based on the available data.


id: cord-004894-75w35fkd
title: Abstract
author: nan
date: 2006-06-14
txt: ./txt/cord-004894-75w35fkd.txt
cache: ./cache/cord-004894-75w35fkd.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7087564/
doi: 10.1007/s10654-006-9021-1
abstract: nan
summary:
  The unadjusted median (25-75% percentile) sperm concentration in the non-exposed group (n = 90) is 49 (23-86) mill/ml compared to 33 (12-63) mill/ml among men exposed to >19 cigarettes per day in fetal life (n = 26 Aim: To estimate the prevalence of overweight and obesity, and their effects in physical activity (PA) levels of Portuguese children and adolescents aged 10-18 years. Objectives: a) To estimate the sex-and age-adjusted annual rate of tuberculosis infection (ARTI) (per 100 person-years [%py]) among the HCWs, as indicated by tuberculin skin test conversion (TST) conversion, b) to identify occupational factors associated with significant variations in the ARTI, c) to investigate the efficacy of the regional preventive guidelines. Objectives: We assessed the total burden of adverse events (AE), and determined treatment-related risk factors for the development of various AEs. Methods: The study cohort included 1362 5-year survivors, treated in the Emma Childrens Hospital AMC in the Netherlands between 1966-1996.


id: cord-007708-hr4smx24
title: Taking Bioinformatics to Systems Medicine
author: van Kampen, Antoine H. C.
date: 2015-08-13
txt: ./txt/cord-007708-hr4smx24.txt
cache: ./cache/cord-007708-hr4smx24.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7120931/
doi: 10.1007/978-1-4939-3283-2_2
summary:
  Second, we discuss how the integration and analysis of multiple types of omics data through integrative bioinformatics may facilitate the determination of more predictive and robust disease signatures, lead to a better understanding of (patho)physiological molecular mechanisms, and facilitate personalized medicine. To enable systems medicine it is necessary to characterize the patient at various levels and, consequently, to collect, integrate, and analyze various types of data including not only clinical (phenotype) and molecular data, but also information about cells (e.g., disease-related alterations in organelle morphology), organs (e.g., lung impedance when studying respiratory disorders such as asthma or chronic obstructive pulmonary disease), and even social networks. Bioinformatics covers many types of analyses including nucleotide and protein sequence analysis, elucidation of tertiary protein structures, quality control, pre-processing and statistical analysis of omics data, determination of genotypephenotype relationships, biomarker identifi cation, evolutionary analysis, analysis of gene regulation, reconstruction of biological networks, text mining of literature and electronic patient records, and analysis of imaging data.

abstract:
  Systems medicine promotes a range of approaches and strategies to study human health and disease at a systems level with the aim of improving the overall well-being of (healthy) individuals, and preventing, diagnosing, or curing disease. In this chapter we discuss how bioinformatics critically contributes to systems medicine. First, we explain the role of bioinformatics in the management and analysis of data. In particular we show the importance of publicly available biological and clinical repositories to support systems medicine studies. Second, we discuss how the integration and analysis of multiple types of omics data through integrative bioinformatics may facilitate the determination of more predictive and robust disease signatures, lead to a better understanding of (patho)physiological molecular mechanisms, and facilitate personalized medicine. Third, we focus on network analysis and discuss how gene networks can be constructed from omics data and how these networks can be decomposed into smaller modules. We discuss how the resulting modules can be used to generate experimentally testable hypotheses, provide insight into disease mechanisms, and lead to predictive models. Throughout, we provide several examples demonstrating how bioinformatics contributes to systems medicine and discuss future challenges in bioinformatics that need to be addressed to enable the advancement of systems medicine.


id: cord-008584-4eylgtbc
title: Evaluating the impact of the weather conditions on the influenza propagation
author: Singh, David E.
date: 2020-04-05
txt: ./txt/cord-008584-4eylgtbc.txt
cache: ./cache/cord-008584-4eylgtbc.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7132999/
doi: 10.1186/s12879-020-04977-w
summary:
  Our goal is to estimate the effects of changes in temperature and relative humidity on the patterns of epidemic influenza based on data provided by the Spanish Influenza Sentinel Surveillance System (SISSS) and the Spanish Meteorological Agency (AEMET). In this work we use the same data sources (SISSS and AEMET agencies) following a different approach: we study some of these relationships from a simulation perspective, considering not only the existing influenza distributions but also the ones related to the climate change. Fig. 10 Effect of short-term changes in the temperature on the influenza propagation for the different communities considered in the simulation One important thing to underline is that the data that the study [5] (whose model we adopt) is based on is of real cases and spans 30 years.

abstract:
  BACKGROUND: Predicting the details of how an epidemic evolves is highly valuable as health institutions need to better plan towards limiting the infection propagation effects and optimizing their prediction and response capabilities. Simulation is a cost- and time-effective way of predicting the evolution of the infection as the joint influence of many different factors: interaction patterns, personal characteristics, travel patterns, meteorological conditions, previous vaccination, etc. The work presented in this paper extends EpiGraph, our influenza epidemic simulator, by introducing a meteorological model as a modular component that interacts with the rest of EpiGraph’s modules to refine our previous simulation results. Our goal is to estimate the effects of changes in temperature and relative humidity on the patterns of epidemic influenza based on data provided by the Spanish Influenza Sentinel Surveillance System (SISSS) and the Spanish Meteorological Agency (AEMET). METHODS: Our meteorological model is based on the regression model developed by AB and JS, and it is tuned with influenza surveillance data obtained from SISSS. After pre-processing this data to clean it and reconstruct missing samples, we obtain new values for the reproduction number of each urban region in Spain, every 10 minutes during 2011. We simulate the propagation of the influenza by setting the date of the epidemic onset and the initial influenza-illness rates for each urban region. RESULTS: We show that the simulation results have the same propagation shape as the weekly influenza rates as recorded by SISSS. We perform experiments for a realistic scenario based on actual meteorological data from 2010-2011, and for synthetic values assumed under simplified predicted climate change conditions. Results show that a diminishing relative humidity of 10% produces an increment of about 1.6% in the final infection rate. The effect of temperature changes on the infection spread is also noticeable, with a decrease of 1.1% per extra degree.Conclusions: Using a tool like ours could help predict the shape of developing epidemics and its peaks, and would permit to quickly run scenarios to determine the evolution of the epidemic under different conditions. We make EpiGraph source code and epidemic data publicly available.


id: cord-009797-8mdie73v
author: Valle, Denis
date: 2018-08-26
txt: ./txt/cord-009797-8mdie73v.txt
cache: ./cache/cord-009797-8mdie73v.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7165608/
doi: 10.1111/gcb.14412
summary:
  title: Extending the Latent Dirichlet Allocation model to presence/absence data: A case study on North American breeding birds and biogeographical shifts expected from climate change The Latent Dirichlet Allocation (LDA) model is a mixed‐membership method that can represent gradual changes in community structure by delineating overlapping groups of species, but its use has been limited because it requires abundance data and requires users to a priori set the number of groups. Furthermore, by comparing the estimated proportion of each group for two time periods (1997–2002 and 2010–2015), our results indicate that nine (of 18) breeding bird groups exhibited an expansion northward and contraction southward of their ranges, revealing subtle but important community‐level biodiversity changes at a continental scale that are consistent with those expected under climate change. It is important to note that even in the absence of MM sampling units, LDA can still estimate well the true number of groups and has similar fit to the data as the other clustering approaches (results not shown).

title:
  Extending the Latent Dirichlet Allocation model to presence/absence data: A case study on North American breeding birds and biogeographical shifts expected from climate change

abstract:
  Understanding how species composition varies across space and time is fundamental to ecology. While multiple methods having been created to characterize this variation through the identification of groups of species that tend to co‐occur, most of these methods unfortunately are not able to represent gradual variation in species composition. The Latent Dirichlet Allocation (LDA) model is a mixed‐membership method that can represent gradual changes in community structure by delineating overlapping groups of species, but its use has been limited because it requires abundance data and requires users to a priori set the number of groups. We substantially extend LDA to accommodate widely available presence/absence data and to simultaneously determine the optimal number of groups. Using simulated data, we show that this model is able to accurately determine the true number of groups, estimate the underlying parameters, and fit with the data. We illustrate this method with data from the North American Breeding Bird Survey (BBS). Overall, our model identified 18 main bird groups, revealing striking spatial patterns for each group, many of which were closely associated with temperature and precipitation gradients. Furthermore, by comparing the estimated proportion of each group for two time periods (1997–2002 and 2010–2015), our results indicate that nine (of 18) breeding bird groups exhibited an expansion northward and contraction southward of their ranges, revealing subtle but important community‐level biodiversity changes at a continental scale that are consistent with those expected under climate change. Our proposed method is likely to find multiple uses in ecology, being a valuable addition to the toolkit of ecologists.


id: cord-010310-jqh75340
title: Next Generation Technology for Epidemic Prevention and Control: Data-Driven Contact Tracking
author: nan
date: 2018-12-24
txt: ./txt/cord-010310-jqh75340.txt
cache: ./cache/cord-010310-jqh75340.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7176034/
doi: 10.1109/access.2018.2882915
summary:
  Furthermore, the transmission networks of infectious diseases established using contact tracking technology can aid in the visualization of actual virus transmission paths, which enables simulations and predictions of the transmission process, assessment of the outbreak trend, and further development and deployment of more effective prevention and control strategies. Tracking the contact interactions of individuals can effectively restore the ''''invisible'''' virus transmission paths, quickly locate and isolate high-risk individuals who were in contact with infected persons, and can aid in quantitative analysis of the transmission paths, processes, and trends of the infectious diseases, all leading to the development of corresponding effective epidemic control strategies. With the aim to collect dynamic, complete, and accurate individual contact information, some researchers began to use mobile phone, wireless sensors, RFID, and GPS devices to track individual contact behaviors. Although detailed individual contact information can be collected through non-automatic methods, e.g., offline and online questionnaire, and automatic methods, e.g., mobile phone, wearable wireless sensors, RFID, and GPS devices.

abstract:
  Contact tracking is one of the key technologies in prevention and control of infectious diseases. In the face of a sudden infectious disease outbreak, contact tracking systems can help medical professionals quickly locate and isolate infected persons and high-risk individuals, preventing further spread and a large-scale outbreak of infectious disease. Furthermore, the transmission networks of infectious diseases established using contact tracking technology can aid in the visualization of actual virus transmission paths, which enables simulations and predictions of the transmission process, assessment of the outbreak trend, and further development and deployment of more effective prevention and control strategies. Exploring effective contact tracking methods will be significant. Governments, academics, and industries have all given extensive attention to this goal. In this paper, we review the developments and challenges of current contact tracing technologies regarding individual and group contact from both static and dynamic perspectives, including static individual contact tracing, dynamic individual contact tracing, static group contact tracing, and dynamic group contact tracing. With the purpose of providing useful reference and inspiration for researchers and practitioners in related fields, directions in multi-view contact tracing, multi-scale contact tracing, and AI-based contact tracing are provided for next-generation technologies for epidemic prevention and control.


id: cord-010406-uwt95kk8
title: System for Infectious Disease Information Sharing and Analysis: Design and Evaluation
author: Hu, Paul Jen-Hwa
date: 2007-07-10
txt: ./txt/cord-010406-uwt95kk8.txt
cache: ./cache/cord-010406-uwt95kk8.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7186032/
doi: 10.1109/titb.2007.893286
summary:
  Motivated by the importance of infectious disease informatics (IDI) and the challenges to IDI system development and data sharing, we design and implement BioPortal, a Web-based IDI system that integrates cross-jurisdictional data to support information sharing, analysis, and visualization in public health. In this paper, we discuss general challenges in IDI, describe BioPortal''s architecture and functionalities, and highlight encouraging evaluation results obtained from a controlled experiment that focused on analysis accuracy, task performance efficiency, user information satisfaction, system usability, usefulness, and ease of use. To support the surveillance and detection of infectious disease outbreaks by public health professionals, we design and implement the BioPortal system, a web-based IDI system that provides convenient access to distributed, cross-jurisdictional health data pertaining to several major infectious diseases including West Nile virus (WNV), foot-and-mouth disease (FMD), and botulism.

abstract:
  Motivated by the importance of infectious disease informatics (IDI) and the challenges to IDI system development and data sharing, we design and implement BioPortal, a Web-based IDI system that integrates cross-jurisdictional data to support information sharing, analysis, and visualization in public health. In this paper, we discuss general challenges in IDI, describe BioPortal's architecture and functionalities, and highlight encouraging evaluation results obtained from a controlled experiment that focused on analysis accuracy, task performance efficiency, user information satisfaction, system usability, usefulness, and ease of use.


id: cord-014833-ax09x6gk
author: Wu, Jia
date: 2016-06-20
txt: ./txt/cord-014833-ax09x6gk.txt
cache: ./cache/cord-014833-ax09x6gk.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7088664/
doi: 10.1007/s11277-016-3438-y
summary:
  title: Data Decision and Transmission Based on Mobile Data Health Records on Sensor Devices in Wireless Networks History data, collection data, and doctor-analyzed data could be computed and transmitted to patients using sensor devices. This study establishes a new method that can decide and transmit effective data based on sensor device mobile health in wireless networks. This study establishes a new method that can decide and transmit effective data based on sensor device mobile health in wireless networks. According to an established mobile health system, patients can obtain timely treatment from doctors or hospitals by using wireless sensor devices. In mobile health, sensor devices and mobile device are the cheapest and most convenient means of data collection and transmission among doctors, patients, and hospitals. Formula (8) assumes that a ¼ 0:15; b ¼ 0:35; c ¼ 0:5: Sensor devices may calculate the probability and transmit diagnosis data to the mobile APP to be evaluated by patients and doctors.

title:
  Data Decision and Transmission Based on Mobile Data Health Records on Sensor Devices in Wireless Networks

abstract:
  The contradiction between a large population and limited and unevenly distributed medical resources is a serious problem in many developing countries. This problem not only affects human health but also leads to the occurrence of serious infection if treatment is delayed. With the development of wireless communication network technology, patients can acquire real-time medical information through wireless network equipment. Patients can have the opportunity to obtain timely medical treatment, which may alleviate the shortage of medical resources in developing countries. This study establishes a new method that can decide and transmit effective data based on sensor device mobile health in wireless networks. History data, collection data, and doctor-analyzed data could be computed and transmitted to patients using sensor devices. According to probability analysis, patients and doctors may confirm the possibility of certain diseases.


id: cord-016140-gvezk8vp
title: Safeguards
author: Ahonen, Pasi
date: 2008
txt: ./txt/cord-016140-gvezk8vp.txt
cache: ./cache/cord-016140-gvezk8vp.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7120333/
doi: 10.1007/978-1-4020-6662-7_5
summary:
  An example is the EC-supported CONNECT project, which aims to implement a privacy management platform within pervasive mobile services, coupling research on semantic technologies and intelligent agents with wireless communications (including UMTS, WiFi and WiMAX) and context-sensitive paradigms and multimodal (voice/graphics) interfaces to provide a strong and secure framework to ensure that privacy is a feasible and desirable component of future ambient intelligence applications. The fast emergence of information and communication technologies and the growth of online communication, e-commerce and electronic services that go beyond the territorial borders of the Member States have led the European Union to adopt numerous legal instruments such as directives, regulations and conventions on ecommerce, consumer protection, electronic signature, cyber crime, liability, data protection, privacy and electronic communication … and many others.

abstract:
  The multiplicity of threats and vulnerabilities associated with AmI will require a multiplicity of safeguards to respond to the risks and problems posed by the emerging technological systems and their applications. In some instances, a single safeguard might be sufficient to address a specified threat or vulnerability. More typically, however, a combination of safeguards will be necessary to address each threat and vulnerability. In still other instances, one safeguard might apply to numerous treats and vulnerabilities. One could depict these combinations in a matrix or on a spreadsheet, but the spreadsheet would quickly become rather large and, perhaps, would be slightly misleading. Just as the AmI world will be dynamic, constantly changing, the applicability of safeguards should also be regarded as subject to a dynamic, i.e., different and new safeguards may need to be introduced in order to cope with changes in the threats and vulnerabilities.


id: cord-016146-2g893c2r
title: Artificial Intelligence Technology and Social Problem Solving
author: Kim, Yeunbae
date: 2019-03-14
txt: ./txt/cord-016146-2g893c2r.txt
cache: ./cache/cord-016146-2g893c2r.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7120339/
doi: 10.1007/978-981-13-6936-0_2
summary:
  In this letter, we will present the views on how AI and ICT technologies can be applied to ease or solve social problems by sharing examples of research results from studies of social anxiety, environmental noise, mobility of the disabled, and problems in social safety. In this letter, I introduce research on the informatics platform for social problem solving, specifically based on spatio-temporal data, conducted by Hanyang University and cooperating institutions. The research focuses on social problems that involve spatio-temporal information, and applies social scientific approaches and data-analytic methods on a pilot basis to explore basic research issues and the validity of the approaches. Furthermore, (1) open-source informatics using convergent-scientific methodology and models, and (2) the spatio-temporal data sets that are to be acquired in the midst of exploring social problems for potential resolution are developed. Convergent approaches offer the new possibility of building an informatics platform that can interpret, predict and solve various social problems through the combination of social science and data science.

abstract:
  Modern societal issues occur in a broad spectrum with very high levels of complexity and challenges, many of which are becoming increasingly difficult to address without the aid of cutting-edge technology. To alleviate these social problems, the Korean government recently announced the implementation of mega-projects to solve low employment, population aging, low birth rate and social safety net problems by utilizing AI and ICBM (IoT, Cloud Computing, Big Data, Mobile) technologies. In this letter, we will present the views on how AI and ICT technologies can be applied to ease or solve social problems by sharing examples of research results from studies of social anxiety, environmental noise, mobility of the disabled, and problems in social safety. We will also describe how all these technologies, big data, methodologies and knowledge can be combined onto an open social informatics platform.


id: cord-016448-7imgztwe
title: Protein-protein interactions: analysis and prediction
author: Frishman, D.
date: 2009-10-01
txt: ./txt/cord-016448-7imgztwe.txt
cache: ./cache/cord-016448-7imgztwe.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7120725/
doi: 10.1007/978-3-211-75123-7_17
summary:
  In general, investigating the topology of protein interaction, metabolic, signaling, and transcriptional networks allows researchers to reveal the fundamental principles of molecular organization of the cell and to interpret genome data in the context of large-scale experiments. The basic principle is fairly simple and rests implicitly on a multigraph representation: several interaction networks to be integrated, each resulting from a specific experimental or predictive method, are defined over the same set of proteins. This software provides functionalities for (i) generating biological networks, either manually or by importing interaction data from various sources, (ii) filtering interactions, (iii) displaying networks using graph layout algorithms, (iv) integrating and displaying additional information like gene expression data, and (v) performing analyses on networks, for instance, by calculating topological network properties or by identifying functional modules. The evidence can be derived from literature mining, functional associations based on Gene Ontology annotations, co-occurrence of transcriptional motifs, correlation of expression data, sequence similarity, common protein domains, shared metabolic pathway membership, and protein-protein interactions.

abstract:
  Proteins represent the tools and appliances of the cell — they assemble into larger structural elements, catalyze the biochemical reactions of metabolism, transmit signals, move cargo across membrane boundaries and carry out many other tasks. For most of these functions proteins cannot act in isolation but require close cooperation with other proteins to accomplish their task. Often, this collaborative action implies physical interaction of the proteins involved. Accordingly, experimental detection, in silico prediction and computational analysis of protein-protein interactions (PPI) have attracted great attention in the quest for discovering functional links among proteins and deciphering the complex networks of the cell.


id: cord-016528-j7lflryj
title: Using Emergency Department Data For Biosurveillance: The North Carolina Experience
author: Waller, Anna E.
date: 2010-07-27
txt: ./txt/cord-016528-j7lflryj.txt
cache: ./cache/cord-016528-j7lflryj.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7120837/
doi: 10.1007/978-1-4419-6892-0_3
summary:
  The benefits and challenges of using Emergency Department data for surveillance are described in this chapter through examples from one biosurveillance system, the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT). With electronic health information systems, these data are available in near real-time, making them particularly useful for surveillance and situational awareness in rapidly developing public health outbreaks or disasters. Biosurveillance is an emerging field that provides early detection of disease outbreaks by collecting and interpreting data on a variety of public health threats, including emerging infectious diseases (e.g., avian influenza), vaccine preventable diseases (e.g., pertussis) and bioterrorism (e.g., anthrax). NC DETECT has since grown to incorporate ED visit data from 98% of 24/7 acute care hospital EDs in the state of North Carolina and has developed and implemented many innovative surveillance tools, including the Emergency Medicine Text Processor (EMT-P) for ED chief complaint data and research-based syndrome definitions.

abstract:
  Biosurveillance is an emerging field that provides early detection of disease outbreaks by collecting and interpreting data on a variety of public health threats. The public health system and medical care community in the United States have wrestled with developing new and more accurate methods for earlier detection of threats to the health of the public. The benefits and challenges of using Emergency Department data for surveillance are described in this chapter through examples from one biosurveillance system, the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT). ED data are a proven tool for biosurveillance, and the ED data in NC DETECT have proved to be effective for a variety of public health uses, including surveillance, monitoring and investigation. A distinctive feature of ED data for surveillance is their timeliness. With electronic health information systems, these data are available in near real-time, making them particularly useful for surveillance and situational awareness in rapidly developing public health outbreaks or disasters. Challenges to using ED data for biosurveillance include the reliance on free text data (often in chief complaints). Problems with textual data are addressed in a variety of ways, including preprocessing data to clean the text entries and address negation. The use of ED data for public health surveillance can significantly increase the speed of detecting, monitoring and investigating public health events. Biosurveillance systems that are incorporated into hospital and public health practitioner daily work flows are more effective and easily used during a public health emergency. The flexibility of a system such as NC DETECT helps it meet this level of functionality.


id: cord-016889-7ih6jdpe
title: Identity Health
author: Shibuya, Kazuhiko
date: 2019-12-03
txt: ./txt/cord-016889-7ih6jdpe.txt
cache: ./cache/cord-016889-7ih6jdpe.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7121317/
doi: 10.1007/978-981-15-2248-2_11
summary:
  These are a kind of mental illnesses and conditions as a maladaptation of gaming and social withdrawals from actual society, or they are overadaptation in somewhat online communities rather than physical environment. Those assessed data might intend to statistically reveal our strength of mental health and degree of adaptation in social relations, and then automatic prediction for those who answered personality tests enables to trustfully measure financial limitations for loans and transactions in actual contexts. (1973) and Giddens (1991) , they commonly argued that western post-modernizations could reconstruct mindsets on reality and social identification ways among citizens during achieving industrial progresses, if above severe incidents of nuclear power plants and those systems failures could be regarded as malfunctions as a symbol of modernity, above consequences of nuclear crisis on the Fukushima case (and other human-made disasters) might be contextualized to reexamine social adaptation and consciousness among Fukushima citizens by sociological verifications. As social networking services clearly indicate a part of human relationships online (Lazakidou 2012) , it can consider that their relations itself still have sharing illness personalities and depressed mental health.

abstract:
  Identity health has especially specific meanings for social relationships in contemporary digital age. First, computerized digital communication makes many citizens in severe maladaptation. The WHO often warns mental addictions of internet usages and online gaming among the youth. The advent of social media and online networking has endangered them in ambiguous situations which are not stabilizing in those basic grounds for human relationships. Further, because social networking sites and social gaming frequently enforce each member to interconnect with the others, many of participating members often hold harder mental debts to respond and maintain their interconnections. In this situation, in other words, it can say that all of users simultaneously might share common conditions under mental illness.


id: cord-017634-zhmnfd1w
title: Infectious Disease Epidemiology
author: Straif-Bourgeois, Susanne
date: 2005
txt: ./txt/cord-017634-zhmnfd1w.txt
cache: ./cache/cord-017634-zhmnfd1w.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122244/
doi: 10.1007/978-3-540-26577-1_34
summary:
  Use of additional clinical, epidemiological and laboratory data may enable a physician to diagnose a disease even though the formal surveillance case definition may not be met. Another way to detect an increase of cases is if the surveillance system of reportable infectious diseases reveals an unusually high number of people with the same diagnosis over a certain time period at different health care facilities. On the other hand, however, there should be no time delay in starting an investigation if there is an opportunity to prevent more cases or the potential to identify a system failure which can be caused, for example, by poor food preparation in a restaurant or poor infection control practices in a hospital or to prevent future outbreaks by acquiring more knowledge of the epidemiology of the agent involved. In developing countries, surveys are often necessary to evaluate health problems since data collected routinely (disease surveillance, hospital records, case registers) are often incomplete and of poor quality.

abstract:
  The following chapter intends to give the reader an overview of the current field of applied infectious disease epidemiology. Prevention of disease by breaking the chain of transmission has traditionally been the main purpose of infectious disease epidemiology. While this goal remains the same, the picture of infectious diseases is changing. New pathogens are identified and already known disease agents are changing their behavior. The world population is aging; more people develop underlying disease conditions and are therefore more susceptible to certain infectious diseases or have long term sequelae after being infected.


id: cord-018133-2otxft31
title: Bioinformatics
author: Altman, Russ B.
date: 2006
txt: ./txt/cord-018133-2otxft31.txt
cache: ./cache/cord-018133-2otxft31.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122933/
doi: 10.1007/0-387-36278-9_22
summary:
  Experimentation and bioinformatics have divided the research into several areas, and the largest are: (1) genome and protein sequence analysis, (2) macromolecular structure-function analysis, (3) gene expression analysis, and (4) proteomics. With the completion of the human genome and the abundance of sequence, structural, and gene expression data, a new field of systems biology that tries to understand how proteins and genes interact at a cellular level is emerging. The Entrez system from the National Center for Biological Information (NCBI) gives integrated access to the biomedical literature, protein, and nucleic acid sequences, macromolecular and small molecular structures, and genome project links (including both the Human Genome Project and sequencing projects that are attempting to determine the genome sequences for organisms that are either human pathogens or important experimental model organisms) in a manner that takes advantages of either explicit or computed links between these data resources.

abstract:
  Why is sequence, structure, and biological pathway information relevant to medicine? Where on the Internet should you look for a DNA sequence, a protein sequence, or a protein structure? What are two problems encountered in analyzing biological sequence, structure, and function? How has the age of genomics changed the landscape of bioinformatics? What two changes should we anticipate in the medical record as a result of these new information sources? What are two computational challenges in bioinformatics for the future?


id: cord-019050-a9datsoo
title: Bioinformatics and Nanotechnologies: Nanomedicine
author: Ambrogi, Federico
date: 2014
txt: ./txt/cord-019050-a9datsoo.txt
cache: ./cache/cord-019050-a9datsoo.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7124100/
doi: 10.1007/978-3-642-30574-0_32
summary:
  In this chapter we focus on the bioinformatics strategies for translating genome-wide expression analyses into clinically useful cancer markers with a specific focus on breast cancer with a perspective on new diagnostic device tools coming from the field of nanobiotechnology and the challenges related to high-throughput data integration, analysis, and assessment from multiple sources. In this chapter we focus on the bioinformatics strategies for translating genome-wide expression analyses into clinically useful cancer markers with a specific focus on breast cancer with a perspective on new diagnostic device tools coming from the field of nanobiotechnology and the challenges related to high-throughput data integration, analysis, and assessment from multiple sources. In particular, DNA microarray-based technology, with the simultaneous evaluation of thousands of genes, has provided researchers with an opportunity to perform comprehensive molecular and genetic profiling of breast cancer able to classify it into some clinically relevant subtypes and in the attempt to predict the prognosis or the response to treatment [32.5-8].

abstract:
  In this chapter we focus on the bioinformatics strategies for translating genome-wide expression analyses into clinically useful cancer markers with a specific focus on breast cancer with a perspective on new diagnostic device tools coming from the field of nanobiotechnology and the challenges related to high-throughput data integration, analysis, and assessment from multiple sources. Great progress in the development of molecular biology techniques has been seen since the discovery of the structure of deoxyribonucleic acid (DNA) and the implementation of a polymerase chain reaction (PCR) method. This started a new era of research on the structure of nucleic acids molecules, the development of new analytical tools, and DNA-based analyses that allowed the sequencing of the human genome, the completion of which has led to intensified efforts toward comprehensive analysis of mammalian cell struc ture and metabolism in order to better understand the mechanisms that regulate normal cell behavior and identify the gene alterations responsible for a broad spectrum of human diseases, such as cancer, diabetes, cardiovascular diseases, neurodegenerative disorders, and others.


id: cord-021088-9u3kn9ge
title: Awaiting the Second Big Data Revolution: From Digital Noise to Value Creation
author: Huberty, Mark
date: 2015-02-18
txt: ./txt/cord-021088-9u3kn9ge.txt
cache: ./cache/cord-021088-9u3kn9ge.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7149011/
doi: 10.1007/s10842-014-0190-4
summary:
  Instead, today''s successful big data business models largely use data to scale old modes of value creation, rather than invent new ones altogether. Four of these assumptions merit special attention: First, N = all, or the claim that our data allow a clear and unbiased study of humanity; second, that today = tomorrow, or the claim that understanding online behavior today implies that we will still understand it tomorrow; third, offline = online, the claim that understanding online behavior offers a window into economic and social phenomena in the physical world; and fourth, that complex patterns of social behavior, once understood, will remain stable enough to become the basis of new data-driven, predictive products and services in sectors well beyond social and media markets. The rate of change in online commerce, social media, search, and other services undermines any claim that we can actually know that our N = all sample that works today will work tomorrow.

abstract:
  “Big data”—the collection of vast quantities of data about individual behavior via online, mobile, and other data-driven services—has been heralded as the agent of a third industrial revolution—one with raw materials measured in bits, rather than tons of steel or barrels of oil. Yet the industrial revolution transformed not just how firms made things, but the fundamental approach to value creation in industrial economies. To date, big data has not achieved this distinction. Instead, today’s successful big data business models largely use data to scale old modes of value creation, rather than invent new ones altogether. Moreover, today’s big data cannot deliver the promised revolution. In this way, today’s big data landscape resembles the early phases of the first industrial revolution, rather than the culmination of the second a century later. Realizing the second big data revolution will require fundamentally different kinds of data, different innovations, and different business models than those seen to date. That fact has profound consequences for the kinds of investments and innovations firms must seek, and the economic, political, and social consequences that those innovations portend.


id: cord-022633-fr55uod6
title: SAEM Abstracts, Plenary Session
author: nan
date: 2012-04-26
txt: ./txt/cord-022633-fr55uod6.txt
cache: ./cache/cord-022633-fr55uod6.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7159364/
doi: 10.1111/j.1553-2712.2012.01332.x
abstract: nan
summary:
  Staff satisfaction was evaluated through pre/ post-shift and study surveys; administrative data (physician initial assessment (PIA), length of stay (LOS), patients leaving without being seen (LWBS) and against medical advice [LAMA] ) were collected from an electronic, real-time ED information system. Communication Background: The link between extended shift lengths, sleepiness, and occupational injury or illness has been shown, in other health care populations, to be an important and preventable public health concern but heretofore has not been fully described in emergency medical services (EMS Objectives: To assess the effect of an ED-based computer screening and referral intervention for IPV victims and to determine what characteristics resulted in a positive change in their safety. Objectives: Using data from longitudinal surveys by the American Board of Emergency Medicine, the primary objective of this study was to evaluate if resident self-assessments of performance in required competencies improve over the course of graduate medical training and in the years following.


id: cord-024058-afgvztwo
author: nan
date: 2015-02-17
txt: ./txt/cord-024058-afgvztwo.txt
cache: ./cache/cord-024058-afgvztwo.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7186037/
doi: 10.1109/jproc.2015.2389146
summary:
  Examples of innovative leveraging of infrastructure, technologies to enhance existing disease management strategies, engineering approaches to accelerate the rate of discovery and application of scientific, clinical, and public health information, and ethical issues that need to be addressed for implementation are presented. Because engineers contribute to the design and implementation of infrastructure, there are opportunities for innovative solutions to infectious disease response within existing systems that have utility, and therefore resources, before a public health emergency. Moving forward, addressing privacy issues will be critical so that geographic tracking of a phone''s location could be used to help inform an individual of potential contact with infected persons or animals and support automated, anonymous, electronic integration of those data to accelerate the epidemiological detective work of identifying and surveying those same individuals for public health benefit.

title:
  Engineering a Global Response to Infectious Diseases: This paper presents a more robust, adaptable, and scalable engineering infrastructure to improve the capability to respond to infectious diseases.Contributed Paper

abstract:
  Infectious diseases are a major cause of death and economic impact worldwide. A more robust, adaptable, and scalable infrastructure would improve the capability to respond to epidemics. Because engineers contribute to the design and implementation of infrastructure, there are opportunities for innovative solutions to infectious disease response within existing systems that have utility, and therefore resources, before a public health emergency. Examples of innovative leveraging of infrastructure, technologies to enhance existing disease management strategies, engineering approaches to accelerate the rate of discovery and application of scientific, clinical, and public health information, and ethical issues that need to be addressed for implementation are presented.


id: cord-024865-umrlsbh5
author: Jiang, Shufan
date: 2020-04-29
txt: ./txt/cord-024865-umrlsbh5.txt
cache: ./cache/cord-024865-umrlsbh5.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7225512/
doi: 10.1007/978-3-030-49165-9_8
summary:
  Our objective is to integrate such heterogeneous data into knowledge bases that can support farmers in their activities, and to present global, real-time and comprehensive information to researchers. Indeed, important information related to agriculture can also come from different sources such as official periodic reports and journals like the French Plants Health Bulletins (BSV, for its name in French Bulletin de Santé du Végétal ) 1 , social media such as Twitter and farmers experiences. The French National Institute For Agricultural Research (INRA) has been working towards the publishing of the bulletins as Linked Open Data [12] , where BSV from different regions are centralized, tagged with crop type, region, date and published on the Internet. We have introduced in this paper work relevant to our problem, namely: the integration of several data sources to extract information related to the natural hazards in agriculture.

title:
  Towards the Integration of Agricultural Data from Heterogeneous Sources: Perspectives for the French Agricultural Context Using Semantic Technologies

abstract:
  Sustainable agriculture is crucial to society since it aims at supporting the world’s current food needs without compromising future generations. Recent developments in Smart Agriculture and Internet of Things have made possible the collection of unprecedented amounts of agricultural data with the goal of making agricultural processes better and more efficient, and thus supporting sustainable agriculture. These data coming from different types of IoT devices can also be combined with relevant information published in online social networks and on the Web in the form of textual documents. Our objective is to integrate such heterogeneous data into knowledge bases that can support farmers in their activities, and to present global, real-time and comprehensive information to researchers. Semantic technologies and linked data provide a possibility for data integration and for automatic information extraction. This paper aims to give a brief review on the current semantic web technology applications for agricultural corpus, then to discuss the limits and potentials in construction and maintenance of existing ontologies in agricultural domain.


id: cord-024866-9og7pivv
author: Lepenioti, Katerina
date: 2020-04-29
txt: ./txt/cord-024866-9og7pivv.txt
cache: ./cache/cord-024866-9og7pivv.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7225513/
doi: 10.1007/978-3-030-49165-9_1
summary:
  The current paper exploits the recent advancements of (deep) machine learning for performing predictive and prescriptive analytics on the basis of enterprise and operational data aiming at supporting the operator on the shopfloor. In this direction, the recent advancements of machine learning can have a substantial contribution in performing predictive and prescriptive analytics on the basis of enterprise and operational data aiming at supporting the operator on the shopfloor and at extracting meaningful insights. The current paper proposes an approach for predictive and prescriptive analytics on the basis of enterprise and operational data for smart manufacturing. 2 presents the background, the challenges and prominent methods for predictive and prescriptive analytics of enterprise and operational data for smart manufacturing.

title:
  Machine Learning for Predictive and Prescriptive Analytics of Operational Data in Smart Manufacturing

abstract:
  Perceiving information and extracting insights from data is one of the major challenges in smart manufacturing. Real-time data analytics face several challenges in real-life scenarios, while there is a huge treasure of legacy, enterprise and operational data remaining untouched. The current paper exploits the recent advancements of (deep) machine learning for performing predictive and prescriptive analytics on the basis of enterprise and operational data aiming at supporting the operator on the shopfloor. To do this, it implements algorithms, such as Recurrent Neural Networks for predictive analytics, and Multi-Objective Reinforcement Learning for prescriptive analytics. The proposed approach is demonstrated in a predictive maintenance scenario in steel industry.


id: cord-024870-79hf7q2r
title: An Architecture for Predictive Maintenance of Railway Points Based on Big Data Analytics
author: Salierno, Giulio
date: 2020-04-29
txt: ./txt/cord-024870-79hf7q2r.txt
cache: ./cache/cord-024870-79hf7q2r.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7225517/
doi: 10.1007/978-3-030-49165-9_3
summary:
  In this paper, we propose a four-layers big data architecture with the goal of establishing a data management policy to manage massive amounts of data produced by railway switch points and perform analytical tasks efficiently. The goal of our work is to design a big data architecture for enabling analytical tasks typical required by the railway industry as well as enabling an effective data management policy to allows end-users to manage huge amounts of data coming from railway lines efficiently. As already mentioned, we considered predictive maintenance as the main task of our architecture; hence to show the effectiveness of the proposed architecture, we use real data collected from points placed over the Italian railway line (Milano -Monza -Chiasso). These log files are heterogeneous in type and contain different information resumed as: Data 3 and 4 are considered to train and evaluate the proposed model to estimate the health status of the points, thus to estimate its RUL (see Sect.

abstract:
  Massive amounts of data produced by railway systems are a valuable resource to enable Big Data analytics. Despite its richness, several challenges arise when dealing with the deployment of a big data architecture into a railway system. In this paper, we propose a four-layers big data architecture with the goal of establishing a data management policy to manage massive amounts of data produced by railway switch points and perform analytical tasks efficiently. An implementation of the architecture is given along with the realization of a Long Short-Term Memory prediction model for detecting failures on the Italian Railway Line of Milano - Monza - Chiasso.


id: cord-025289-lhjn97f7
title: StreamPipes Connect: Semantics-Based Edge Adapters for the IIoT
author: Zehnder, Philipp
date: 2020-05-07
txt: ./txt/cord-025289-lhjn97f7.txt
cache: ./cache/cord-025289-lhjn97f7.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7250600/
doi: 10.1007/978-3-030-49461-2_39
summary:
  To mitigate these challenges, we present StreamPipes Connect, targeted at domain experts to ingest, harmonize, and share time series data as part of our industry-proven open source IIoT analytics toolbox StreamPipes. Our main contributions are (i) a semantic adapter model including automated transformation rules for pre-processing, and (ii) a distributed architecture design to instantiate adapters at edge nodes where the data originates. The goal of this paper is to simplify the process of connecting new sources, harmonize data, as well as to utilize semantic meta-information about its meaning, by providing a system with a graphical user interface (GUI). Based on this model, adapters are instantiated, to connect and harmonize data according to pre-processing rules applied to each incoming event. Generated adapters connect to the configured data sources and pre-process data directly at the edge by applying pipelines consisting of user-defined transformation rules.

abstract:
  Accessing continuous time series data from various machines and sensors is a crucial task to enable data-driven decision making in the Industrial Internet of Things (IIoT). However, connecting data from industrial machines to real-time analytics software is still technically complex and time-consuming due to the heterogeneity of protocols, formats and sensor types. To mitigate these challenges, we present StreamPipes Connect, targeted at domain experts to ingest, harmonize, and share time series data as part of our industry-proven open source IIoT analytics toolbox StreamPipes. Our main contributions are (i) a semantic adapter model including automated transformation rules for pre-processing, and (ii) a distributed architecture design to instantiate adapters at edge nodes where the data originates. The evaluation of a conducted user study shows that domain experts are capable of connecting new sources in less than a minute by using our system. The presented solution is publicly available as part of the open source software Apache StreamPipes.


id: cord-025506-yoav2b35
title: PolicyCLOUD: Analytics as a Service Facilitating Efficient Data-Driven Public Policy Management
author: Kyriazis, Dimosthenis
date: 2020-05-06
txt: ./txt/cord-025506-yoav2b35.txt
cache: ./cache/cord-025506-yoav2b35.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256368/
doi: 10.1007/978-3-030-49161-1_13
summary:
  Prominent examples of such standards in different policy areas include: (i) the INSPIRE Data Specifications [15] for the interoperability of spatial data sets and services, which specify common data models, code lists, map layers and additional metadata on the interoperability to be used when exchanging spatial datasets, (ii) the Common European Research Information Format (CERIF) [16] for representing research information and supporting research policies, (iii) the Internet of Things ontologies and schemas, such as the W3C Semantic Sensor Networks (SSN) ontology [17] and data schemas developed by the Open Geospatial Consortium (e.g., SensorML) [18], (iv) the Common Reporting Standard (CRS) that specifies guidelines for obtaining information from financial institutions and automatically exchanging that information in an interoperable way, and (v) standards-based ontologies appropriate for describing social relationships between individuals or groups, such as the "The Friend Of A Friend" (FOAF) ontology [19] and the Socially Interconnected Online Communities (SIOC) ontology [20] .

abstract:
  While several application domains are exploiting the added-value of analytics over various datasets to obtain actionable insights and drive decision making, the public policy management domain has not yet taken advantage of the full potential of the aforementioned analytics and data models. Diverse and heterogeneous datasets are being generated from various sources, which could be utilized across the complete policies lifecycle (i.e. modelling, creation, evaluation and optimization) to realize efficient policy management. To this end, in this paper we present an overall architecture of a cloud-based environment that facilitates data retrieval and analytics, as well as policy modelling, creation and optimization. The environment enables data collection from heterogeneous sources, linking and aggregation, complemented with data cleaning and interoperability techniques in order to make the data ready for use. An innovative approach for analytics as a service is introduced and linked with a policy development toolkit, which is an integrated web-based environment to fulfil the requirements of the public policy ecosystem stakeholders.


id: cord-025519-265qdtw6
title: A Two-Levels Data Anonymization Approach
author: Zouinina, Sarah
date: 2020-05-06
txt: ./txt/cord-025519-265qdtw6.txt
cache: ./cache/cord-025519-265qdtw6.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256381/
doi: 10.1007/978-3-030-49161-1_8
summary:
  Consequently, privacy preservation through machine learning algorithms were designed based on cryptography, statistics, databases modeling and data mining. To this purpose, we revisited all the previously proposed approaches, and we added a second level of anonymization by incorporating the discriminative information and using Adaptive Weighting of Features to improve the quality of the anonymized data. The paper is organised into four sections: the first dresses the different approaches of privacy preserving using machine learning, the second sums up the previously proposed approaches, the third discusses the introduction of the discriminative information and the fourth validates the method experimentally on six different datasets. The two models propose an algorithm that relies on the classical Self Organizing Maps (SOMs) [10] and collaborative Multiview clustering in purpose to provide useful anonymous datasets [9] . As shown in the Table 5 , the introduction of the discriminant information improves the utility of the anonymized datasets for all of the methods proposed.

abstract:
  The amount of devices gathering and using personal data without the person’s approval is exponentially growing. The European General Data Protection Regulation (GDPR) came following the requests of individuals who felt at risk of personal privacy breaches. Consequently, privacy preservation through machine learning algorithms were designed based on cryptography, statistics, databases modeling and data mining. In this paper, we present two-levels data anonymization methods. The first level consists of anonymizing data using an unsupervised learning protocol, and the second level is anonymization by incorporating the discriminative information to test the effect of labels on the quality of the anonymized data. The results show that the proposed approaches give good results in terms of utility what preserves the trade-off between data privacy and its usefulness.


id: cord-025545-s6t9a7z8
title: Using Classification for Traffic Prediction in Smart Cities
author: Christantonis, Konstantinos
date: 2020-05-06
txt: ./txt/cord-025545-s6t9a7z8.txt
cache: ./cache/cord-025545-s6t9a7z8.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256407/
doi: 10.1007/978-3-030-49161-1_5
summary:
  This work focuses on analyzing different approaches regarding data manipulation in order to predict day-ahead traffic loads at random places around cities, based on weather conditions. Based on that, we used weather data collected from sensors installed around carefully chosen specific city spots for predicting the day-ahead traffic volume. To select the most appropriate locations to install sensors that either measure traffic loads or collect weather data, it is crucial to define their objective in advance. Our efforts focus on the question ''How can one exploit sensor data that are not personalized and create meaningful conclusions for the general public?'' Deployment of smart city infrastructure requires a deep understanding of the traffic problem. Our approach, besides examining traffic predictability based on weather data, also aims to clarifying differences among locations. For example, if a sensor captures information every h(e.g. at 07:10, 08:10, 09:10 etc.), we computed and assigned the average value for each weather metric and the traffic load for that specific day period.

abstract:
  Smart cities emerge as highly sophisticated bionetworks, providing smart services and ground-breaking solutions. This paper relates classification with Smart City projects, particularly focusing on traffic prediction. A systematic literature review identifies the main topics and methods used, emphasizing on various Smart Cities components, such as data harvesting and data mining. It addresses the research question whether we can forecast traffic load based on past data, as well as meteorological conditions. Results have shown that various models can be developed based on weather data with varying level of success.


id: cord-025550-nr3goxs5
title: Towards a Smart Port: The Role of the Telecom Industry
author: Gizelis, Christos-Antonios
date: 2020-05-04
txt: ./txt/cord-025550-nr3goxs5.txt
cache: ./cache/cord-025550-nr3goxs5.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256412/
doi: 10.1007/978-3-030-49190-1_12
summary:
  "DataPorts project aims to boost the transition of European seaports from connected and digital to smart and cognitive, by providing a secure environment for the aggregation and integration of data coming from different sources existing in the digital ports and owned by diverse stakeholders, so that the whole port community could benefit from this data in order to improve their processes, offer new services and devise new AI based and data driven business models" [10] . A Telecom/ICT Provider in order to enter this emerging ecosystem and potentially benefit from its growth should firstly address real-life data market use cases in Ports that are related to its areas of operations. DataPorts since January 2020 is planning to implement a data management platform to be operated by Port Authorities in order to provide advanced services (Fig. 1) and create a value-chain between stakeholders, internal and external ones (Fig. 2 ).

abstract:
  Transformation is not only today’s trend but also a reality. Ports could not be excluded from that change. A transformation process has been initiated in order to change their operational structure, and the services they offer. Artificial Intelligent and Data oriented services push the services’s landscape beyond the traditional ones that are currently used. The scope of this paper is to analyze and scrutinize the opportunities that are risen for Telecommunications/Information and Communication Technology (ICT) providers at ports. These opportunities are the stepping stone towards the transformation of ports for the future. This work in progress is under the DataPorts project that is funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 871493.


id: cord-025576-8oqfn4rg
title: Towards Fashion Recommendation: An AI System for Clothing Data Retrieval and Analysis
author: Kotouza, Maria Th.
date: 2020-05-06
txt: ./txt/cord-025576-8oqfn4rg.txt
cache: ./cache/cord-025576-8oqfn4rg.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256565/
doi: 10.1007/978-3-030-49186-4_36
summary:
  The system combines natural language processing (NLP) techniques to analyze the information accompanying the clothing images, computer vision algorithms to extract characteristics from the images and enrich their meta-data, and machine learning techniques to analyze the raw data and to train models that can facilitate the decision-making process. Several research works have been presented in the field of clothing data analysis, most of them involving clothing classification and feature extraction based on images, dataset creation, as well as product recommendation. In this work, apart from proposing an AI system which involves many subsystems as part of the clothing design process that can be combined together in order to help the designers with the decision-making process, we emphasize on the data collection, meta-data analysis and clustering techniques that can be applied to improve recommendations.

abstract:
  Nowadays, the fashion industry is moving towards fast fashion, offering a large selection of garment products in a quicker and cheaper manner. To this end, the fashion designers are required to come up with a wide and diverse amount of fashion products in a short time frame. At the same time, the fashion retailers are oriented towards using technology, in order to design and provide products tailored to their consumers’ needs, in sync with the newest fashion trends. In this paper, we propose an artificial intelligence system which operates as a personal assistant to a fashion product designer. The system’s architecture and all its components are presented, with emphasis on the data collection and data clustering subsystems. In our use case scenario, datasets of garment products are retrieved from two different sources and are transformed into a specific format by making use of Natural Language Processes. The two datasets are clustered separately using different mixed-type clustering algorithms and comparative results are provided, highlighting the usefulness of the clustering procedure in the clothing product recommendation problem.


id: cord-025827-vzizkekp
title: Data Sovereignty and the Internet of Production
author: Jarke, Matthias
date: 2020-05-09
txt: ./txt/cord-025827-vzizkekp.txt
cache: ./cache/cord-025827-vzizkekp.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7266444/
doi: 10.1007/978-3-030-49435-3_34
summary:
  2006) to the inter-organizational setting by introducing the idea of Industrial Data Spaces as the kernel of platforms in which specific industrial ecosystems could organize their cooperation in a data-sovereign manner (Jarke 2017; Jarke and Quix 2017) . Via numerous use case experiments, the International Data Space (IDS) Association with currently roughly 100 corporate members worldwide has evolved, and agreed on a reference architecture now already in version 3 . In Fig. 1 , we referred to the service-dominant business logic underlying most alliance-driven data ecosystems including the IDS. In this 7-year effort, 27 research groups from production and materials engineering, computer science, business and social sciences cooperate to study not just the sovereign data exchange addressed by the IDS Architecture in a fully globalized setting, but also the question of how to communicate between model-and data-driven approaches of vastly different disciplines and scales.

abstract:
  While the privacy of personal data has captured great attention in the public debate, resulting, e.g., in the European GDPR guideline, the sovereignty of knowledge-intensive small and medium enterprises concerning the usage of their own data in the presence of dominant data-hungry players in the Internet needs more investigation. In Europe, even the legal concept of data ownership is unclear. We reflect on requirements analyses, reference architectures and solution concepts pursued by the International Data Spaces Initiative to address these issues. The second part will more deeply explore our current interdisciplinary research in a visionary “Internet of Production” with 27 research groups from production and materials engineering, computer science, business and social sciences. In this setting, massive amounts of heterogeneous data must be exchanged and analyzed across organizational and disciplinary boundaries, throughout the lifecycle from (re-)engineering, to production, usage and recycling, under hard resource and time constraints. A shared metaphor, borrowed from Plato’s famous Cave Allegory, serves as the core modeling and data management approach from conceptual, logical, physical, and business perspectives.


id: cord-026356-zm84yipu
title: Fed-DIC: Diagonally Interleaved Coding in a Federated Cloud Environment
author: Tzouros, Giannis
date: 2020-05-15
txt: ./txt/cord-026356-zm84yipu.txt
cache: ./cache/cord-026356-zm84yipu.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7276260/
doi: 10.1007/978-3-030-50323-9_4
summary:
  In this paper we present Fed-DIC, a framework which combines Diagonally Interleaved Coding on client devices at the edge of the network with organized storage of encoded data in a federated cloud system comprised of multiple independent storage clusters. Yet the most critical challenge with erasure coding is that it suffers from high reconstruction cost as it needs to access multiple blocks stored across different sets of storage nodes or racks (groups of nodes inside a distributed system) in order to retrieve lost data [7] , leading to high read access and network bandwidth latency. Fed-DIC''s topology in terms of the stored data among the clusters of the federated cloud, combined with the reduced storage size of the data chunks generated from its encoding process, provide significantly smaller read access costs and transfer bandwidth overhead for nodes in the cloud.

abstract:
  Coping with failures in modern distributed storage systems that handle massive volumes of heterogeneous and potentially rapidly changing data, has become a very important challenge. A common practice is to utilize fault tolerance methods like Replication and Erasure Coding for maximizing data availability. However, while erasure codes provide better fault tolerance compared to replication with a more affordable storage overhead, they frequently suffer from high reconstruction cost as they require to access all available nodes when a data block needs to be repaired, and also can repair up to a limited number of unavailable data blocks, depending on the number of the code’s parity block capabilities. Furthermore, storing and placing the encoded data in the federated storage system also remains a challenge. In this paper we present Fed-DIC, a framework which combines Diagonally Interleaved Coding on client devices at the edge of the network with organized storage of encoded data in a federated cloud system comprised of multiple independent storage clusters. The erasure coding operations are performed on client devices at the edge while they interact with the federated cloud to store the encoded data. We describe how our solution integrates the functionality of federated clouds alongside erasure coding implemented on edge devices for maximizing data availability and we evaluate the working and benefits of our approach in terms of read access cost, data availability, storage overhead, load balancing and network bandwidth rate compared to popular Replication and Erasure Coding schemes.


id: cord-026935-586w2cam
author: Fang, Zhichao
date: 2020-06-17
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297939/
doi: 10.1007/s11192-020-03564-9
title:
  An extensive analysis of the presence of altmetric data for Web of Science publications across subject fields and research topics

abstract:
  Sufficient data presence is one of the key preconditions for applying metrics in practice. Based on both Altmetric.com data and Mendeley data collected up to 2019, this paper presents a state-of-the-art analysis of the presence of 12 kinds of altmetric events for nearly 12.3 million Web of Science publications published between 2012 and 2018. Results show that even though an upward trend of data presence can be observed over time, except for Mendeley readers and Twitter mentions, the overall presence of most altmetric data is still low. The majority of altmetric events go to publications in the fields of Biomedical and Health Sciences, Social Sciences and Humanities, and Life and Earth Sciences. As to research topics, the level of attention received by research topics varies across altmetric data, and specific altmetric data show different preferences for research topics, on the basis of which a framework for identifying hot research topics is proposed and applied to detect research topics with higher levels of attention garnered on certain altmetric data source. Twitter mentions and policy document citations were selected as two examples to identify hot research topics of interest of Twitter users and policy-makers, respectively, shedding light on the potential of altmetric data in monitoring research trends of specific social attention.


id: cord-027431-6twmcitu
title: Spatiotemporal Filtering Pipeline for Efficient Social Networks Data Processing Algorithms
author: Mukhina, Ksenia
date: 2020-05-25
txt: ./txt/cord-027431-6twmcitu.txt
cache: ./cache/cord-027431-6twmcitu.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304753/
doi: 10.1007/978-3-030-50433-5_7
summary:
  To do that we propose a spatiotemporal data processing pipeline that is general enough to fit most of the problems related to working with LBSNs. The proposed pipeline includes four main stages: an identification of suspicious profiles, a background extraction, a spatial context extraction, and a fake transitions detection. Efficiency of the pipeline is demonstrated on three practical applications using different LBSN: touristic itinerary generation using Facebook locations, sentiment analysis of an area with the help of Twitter and VK.com, and multiscale events detection from Instagram posts. Thus, all studies based on social networks as a data source face two significant issues: wrong location information stored in the service (wrong coordinates, incorrect titles, duplicates, etc.) and false information provided by users (to hide an actual position or to promote their content).

abstract:
  One of the areas that gathers momentum is the investigation of location-based social networks (LBSNs) because the understanding of citizens’ behavior on various scales can help to improve quality of living, enhance urban management, and advance the development of smart cities. But it is widely known that the performance of algorithms for data mining and analysis heavily relies on the quality of input data. The main aim of this paper is helping LBSN researchers to perform a preliminary step of data preprocessing and thus increase the efficiency of their algorithms. To do that we propose a spatiotemporal data processing pipeline that is general enough to fit most of the problems related to working with LBSNs. The proposed pipeline includes four main stages: an identification of suspicious profiles, a background extraction, a spatial context extraction, and a fake transitions detection. Efficiency of the pipeline is demonstrated on three practical applications using different LBSN: touristic itinerary generation using Facebook locations, sentiment analysis of an area with the help of Twitter and VK.com, and multiscale events detection from Instagram posts.


id: cord-027704-zm1nae6h
author: Vito, Domenico
date: 2020-05-31
txt: ./txt/cord-027704-zm1nae6h.txt
cache: ./cache/cord-027704-zm1nae6h.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7313275/
doi: 10.1007/978-3-030-51517-1_39
summary:
  In the year 2015 ITU and the United Nations Economic Commission for Europe (UNECE) gave the definition of smart and sustainable city as "an innovative city that uses information and communication technologies (ICTs) and other means to improve quality of life, efficiency of urban operation and services, and competitiveness, while ensuring that it meets the needs of present and future generations with respect to economic, social, environmental as well as cultural aspects". The project is currently active in eight pilot cities, Barcelona, Birmingham, New York, Paris, Singapore, Pavia, Keelung and Taiwan, following a participatory approach where citizen provide data through personal devices and the PulsAIR app, that are integrated with information from heterogeneous sources: open city data, health systems, urban sensors and satellites. The clinical is on asthma and Type 2 Diabetes in adult populations: the project has been pioneer in the development of dynamic spatiotemporal health impact assessments through exposure-risk simulation model with the support of WebGis for geolocated population-based data.

title:
  The PULSE Project: A Case of Use of Big Data Uses Toward a Cohomprensive Health Vision of City Well Being

abstract:
  Despite the silent effects sometimes hidden to the major audience, air pollution is becoming one of the most impactful threat to global health. Cities are the places where deaths due to air pollution are concentrated most. In order to correctly address intervention and prevention thus is essential to assest the risk and the impacts of air pollution spatially and temporally inside the urban spaces. PULSE aims to design and build a large-scale data management system enabling real time analytics of health, behaviour and environmental data on air quality. The objective is to reduce the environmental and behavioral risk of chronic disease incidence to allow timely and evidence-driven management of epidemiological episodes linked in particular to two pathologies; asthma and type 2 diabetes in adult populations. developing a policy-making across the domains of health, environment, transport, planning in the PULSE test bed cities.


id: cord-027712-2o4svbms
author: Urošević, Vladimir
date: 2020-05-31
txt: ./txt/cord-027712-2o4svbms.txt
cache: ./cache/cord-027712-2o4svbms.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7313283/
doi: 10.1007/978-3-030-51517-1_13
summary:
  We present the grounding approach, deployment and preliminary validation of the elementary devised model of physical well-being in urban environments, summarizing the heterogeneous personal Big Data (on physical activity/exercise, walking, cardio-respiratory fitness, quality of sleep and related lifestyle and health habits and status, continuously collected for over a year mainly through wearable IoT devices and survey instruments in 7 global testbed cities) into 5 composite domain indicators/indexes convenient for interpretation and use in predictive public health and preventive interventions. In the first approach, daily and intra-daily underlying measurements (Table 1 ) are used to estimate levels of adherence to rule-and range-based recommendations matured from institutional knowledge of relevant authorities and population-significant studies in the field, accumulated for over decades in the stated four example domains of motility, physical activity, sleep quality and cardio-respiratory fitness [8, 10, 11] .

title:
  Baseline Modelling and Composite Representation of Unobtrusively (IoT) Sensed Behaviour Changes Related to Urban Physical Well-Being

abstract:
  We present the grounding approach, deployment and preliminary validation of the elementary devised model of physical well-being in urban environments, summarizing the heterogeneous personal Big Data (on physical activity/exercise, walking, cardio-respiratory fitness, quality of sleep and related lifestyle and health habits and status, continuously collected for over a year mainly through wearable IoT devices and survey instruments in 7 global testbed cities) into 5 composite domain indicators/indexes convenient for interpretation and use in predictive public health and preventive interventions. The approach is based on systematized comprehensive domain knowledge implemented through range/threshold-based rules from institutional and study recommendations, combined with statistical methods, and will serve as a representative and performance benchmark for evolution and evaluation of more complex and advanced well-being models for the aimed predictive analytics (incorporating machine learning methods) in subsequent development underway.


id: cord-028802-ko648mzz
title: Big Data and Reality Mining in Healthcare: Promise and Potential
author: Asri, Hiba
date: 2020-06-05
txt: ./txt/cord-028802-ko648mzz.txt
cache: ./cache/cord-028802-ko648mzz.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340942/
doi: 10.1007/978-3-030-51935-3_13
summary:
  We illustrate the benefits of reality mining analytics that lead to promote patients'' health, enhance medicine, reduce cost and improve healthcare value and quality. This paper gives insight on the challenges and opportunities related to analyzing larger amounts of health data and creating value from it, the capability of reality mining in predicting outcomes and saving lives, and the Big Data tools needed for analysis and processing. Reality Mining is about using big data to study our behavior through mobile phone and wearable sensors [4] . Another study use pregnant woman''s mobile phone health data like user''s activity, user''s sleep quality, user''s location, user''s age, user''s Body Mass Index (BMI)among others, considered as risk factors of miscarriage, in order to make an early prediction of miscarriage and react as earlier as possible to prevent it. The use of both Big data and reality mining in healthcare industry has the capability to provide new opportunities with respect to patients, treatment monitoring, healthcare service and diagnosis.

abstract:
  Nowadays individuals are creating a huge amount of data; with a cell phone in every pocket, a laptop in every bag and wearable sensors everywhere, the fruits of the information are easy to see but less noticeable is the information itself. This data could be particularly useful in making people’s lives healthier and easier, by contributing not only to understand new diseases and therapies but also to predict outcomes at earlier stages and make real-time decisions. In this paper, we explain the potential benefits of big data to healthcare and explore how it improves treatment and empowers patients, providers and researchers. We also describe the capabilities of reality mining in terms of individual health, social network mapping, behavior patterns and treatment monitoring. We illustrate the benefits of reality mining analytics that lead to promote patients’ health, enhance medicine, reduce cost and improve healthcare value and quality. Furthermore, we highlight some challenges that big data analytics faces in healthcare.


id: cord-029865-zl0romvl
title: Learning from lines: Critical COVID data visualizations and the quarantine quotidian
author: Bowe, Emily
date: 2020-07-27
txt: ./txt/cord-029865-zl0romvl.txt
cache: ./cache/cord-029865-zl0romvl.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7387849/
doi: 10.1177/2053951720939236
summary:
  In response to the ubiquitous graphs and maps of COVID-19, artists, designers, data scientists, and public health officials are teaming up to create counter-plots and subaltern maps of the pandemic. Together, the official maps and counter-plots acknowledge that the pandemic plays out differently across different scales: COVID-19 is about global supply chains and infection counts and TV ratings for presidential press conferences, but it is also about local dynamics and neighborhood mutual aid networks and personal geographies of mitigation and care. The widespread availability of consumer-friendly mapping platforms and open data repositories has equipped cartographers and information designers to plot their own charts and graphs-some of which then circulate on social media or appear on slide shows at official public health briefings (Bazzaz, 2020; Mattern, 2020a; "Triplet Kids," 2020) . Available at: www.medium.com/nightingale/covid-19-data-literacy-isfor-everyone-46120b58cec9 Available at: www.expressnews.com/news/local/article/Thousands-h it-hard-by-coronavirus-pandemic-s-15189948

abstract:
  In response to the ubiquitous graphs and maps of COVID-19, artists, designers, data scientists, and public health officials are teaming up to create counter-plots and subaltern maps of the pandemic. In this intervention, we describe the various functions served by these projects. First, they offer tutorials and tools for both dataviz practitioners and their publics to encourage critical thinking about how COVID-19 data is sourced and modeled—and to consider which subjects are not interpellated in those data sets, and why not. Second, they demonstrate how the pandemic’s spatial logics inscribe themselves in our immediate material landscapes. And third, they remind us of our capacity to personalize and participate in the creation of meaningful COVID visualizations—many of which represent other scales and dimensions of the pandemic, especially the quarantine quotidian. Together, the official maps and counter-plots acknowledge that the pandemic plays out differently across different scales: COVID-19 is about global supply chains and infection counts and TV ratings for presidential press conferences, but it is also about local dynamics and neighborhood mutual aid networks and personal geographies of mitigation and care.


id: cord-030772-swha1e4m
title: Interpreting big-data analysis of retrospective observational data
author: Huizinga, Tom W J
date: 2020-08-21
txt: ./txt/cord-030772-swha1e4m.txt
cache: ./cache/cord-030772-swha1e4m.txt
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mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442429/
doi: 10.1016/s2665-9913(20)30289-7
abstract: nan
summary:
  1 In The Lancet Rheumatology, Jennifer Lane and col leagues present a study using claims data and elec tronic medical records (mostly of patients with rheuma toid arthritis) to analyse the longterm risks of cardiovas cular complications (among other outcomes) in about 1 000 000 users of hydroxychloroquine compared with more than 300 000 users of sulfasalazine. It has been convincingly shown that most published data are false, 4 and the corollary that the hotter a scientific field (with more scientific teams involved), the less likely the research findings are to be true is a relevant consideration given the recent discussions around use of hydroxychloroquine in patients with COVID19. It is important to note that the authors used stateoftheart methods to deal with the chal lenges of studying retrospective electronic medical record data; they did a newuser cohort study and a selfcontrolled case series to avoid the risk of bias in a casecontrol design, using propensity scores, fitting models with ten-fold cross validation, and negative control outcome analyses.


id: cord-032383-2dqpxumn
title: COVID-19 open source data sets: a comprehensive survey
author: Shuja, Junaid
date: 2020-09-21
txt: ./txt/cord-032383-2dqpxumn.txt
cache: ./cache/cord-032383-2dqpxumn.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7503433/
doi: 10.1007/s10489-020-01862-6
summary:
  Our survey is motivated by the open source efforts that can be mainly categorized as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. Automated CT scan based COVID-19 detection techniques work with training the learning model on existing CT scan data sets that contain labeled images of COVID-19 positive and normal cases. Triggered by this challenge limiting the adoption of AI/ML-powered COVID-19 diagnosis, forecasting, and mitigation, we make the first effort in surveying research works based on open source data sets concerning COVID-19 pandemic. The authors enlist the application of deep and transfer learning on their extracted data set for identification of COVID-19 while utilizing motivation from earlier studies that learned the type of pneumonia from similar images [47] .

abstract:
  In December 2019, a novel virus named COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica, causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential global crisis since the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligence-based analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We survey and compare research works in these directions that are accompanied by open source data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will provide the scientific community with an initiative to start open source extensible and transparent research in the collective fight against the COVID-19 pandemic.


id: cord-032403-9c1xeqg1
author: Sokolov, Michael
date: 2020-09-08
txt: ./txt/cord-032403-9c1xeqg1.txt
cache: ./cache/cord-032403-9c1xeqg1.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7507805/
doi: 10.1021/acs.iecr.0c02994
summary:
  10 The main engineering challenges 9,11−13 are to (1) robustly control the behavior of the living organism involved in the process, (2) efficiently align the often heterogeneous data generated across different process units and scales, (3) include all available prior know-how and experience into the decision process, (4) reduce human errors and introduced inconsistency, and (5) enable an automated and adaptive procedure to assess the critical process characteristics. Because of significant time pressure in development and risk mitigation pressure in manufacturing, decisions are often made on an ad hoc basis involving expert meetings where all readily available data, analysis results, and experience sources are taken into account without ensuring consideration of all possible available information hidden in the databases or inside the potential of (not automatedly retrained or connected) predictive models. However, in manufacturing operations which are based on Industrial & Engineering Chemistry Research pubs.acs.org/IECR Commentary decisions either actively introduced or supported by such models, a detailed assessment of these smart digital solutions is required.

title:
  Decision Making and Risk Management in Biopharmaceutical Engineering—Opportunities in the Age of Covid-19 and Digitalization

abstract:
  [Image: see text] In 2020, the Covid-19 pandemic resulted in a worldwide challenge without an evident solution. Many persons and authorities involved befriended the value of available data and established expertise to make decisions under time pressure. This omnipresent example is used to illustrate the decision-making procedure in biopharmaceutical manufacturing. This commentary addresses important challenges and opportunities to support risk management in biomanufacturing through a process-centered digitalization approach combining two vital worlds—formalized engineering fundamentals and data empowerment through customized machine learning. With many enabling technologies already available and first success stories reported, it will depend on the interaction of different groups of stakeholders how and when the huge potential of the discussed technologies will be broadly and systematically realized.


id: cord-032607-bn8g02gi
author: Wake, Melissa
date: 2020-09-24
txt: ./txt/cord-032607-bn8g02gi.txt
cache: ./cache/cord-032607-bn8g02gi.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512047/
doi: 10.1186/s12874-020-01111-x
summary:
  Keywords: Research methodology, Randomization, Registry trials, Multiple baseline randomized trials, Trials within cohorts, Population studies, Generation Victoria (GenV), Clinical trial as topic, Children, Intervention Background Randomized controlled trials (RCT) provide high-quality evidence with regards to the effectiveness of therapies and prevention and are critical to guide translation and optimal resource allocation. If feasibility (potentially demonstrated through pilot studies) and mutual alignment appear likely [29] , the trial would proceed to a partnering agreement that defines at least the following 8 items: 1) Which GenV trial model is being followed; 2) Design and high-level (or draft) protocol; 3) Timelines; 4) Data sharing and governance plans; 5) Status of ethical approval; 6) Communication with participants, including information statement and consent; 7) Trial oversight and 8) Capacity assessment, including trial quality, human resource and funding.

title:
  Integrating trials into a whole-population cohort of children and parents: statement of intent (trials) for the Generation Victoria (GenV) cohort

abstract:
  BACKGROUND: Very large cohorts that span an entire population raise new prospects for the conduct of multiple trials that speed up advances in prevention or treatment while reducing participant, financial and regulatory burden. However, a review of literature reveals no blueprint to guide this systematically in practice. This Statement of Intent proposes how diverse trials may be integrated within or alongside Generation Victoria (GenV), a whole-of-state Australian birth cohort in planning, and delineates potential processes and opportunities. METHODS: Parents of all newborns (estimated 160,000) in the state of Victoria, Australia, will be approached for two full years from 2021. The cohort design comprises four elements: (1) consent soon after birth to follow the child and parent/s until study end or withdrawal; retrospective and prospective (2) linkage to clinical and administrative datasets and (3) banking of universal and clinical biosamples; and (4) GenV-collected biosamples and data. GenV-collected data will focus on overarching outcome and phenotypic measures using low-burden, universal-capable electronic interfaces, with funding-dependent face-to-face assessments tailored to universal settings during the early childhood, school and/or adult years. RESULTS: For population or registry-type trials within GenV, GenV will provide all outcomes data and consent via traditional, waiver, or Trials Within Cohorts models. Trials alongside GenV consent their own participants born within the GenV window; GenV may help identify potential participants via opt-in or opt-out expression of interest. Data sharing enriches trials with outcomes, prior data, and/or access to linked data contingent on custodian’s agreements, and supports modeling of causal effects to the population and between-trials comparisons of costs, benefits and utility. Data access will operate under the Findability, Accessibility, Interoperability, and Reusability (FAIR) and Care and Five Safes Principles. We consider governance, ethical and shared trial oversight, and expectations that trials will adhere to the best practice of the day. CONCLUSIONS: Children and younger adults can access fewer trials than older adults. Integrating trials into mega-cohorts should improve health and well-being by generating faster, larger-scale evidence on a longer and/or broader horizon than previously possible. GenV will explore the limits and details of this approach over the coming years.


id: cord-032763-cdhu2pfi
title: Location Data as Contractual Counter-Performance: A Consumer Perspective on Recent EU Legislation
author: Efroni, Zohar
date: 2020-06-22
txt: ./txt/cord-032763-cdhu2pfi.txt
cache: ./cache/cord-032763-cdhu2pfi.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7521238/
doi: 10.1007/978-3-662-61920-9_13
summary:
  38 Therefore, this Regulation should require providers of electronic communications services to obtain end-users'' consent to process electronic communications metadata, which should include data on the location of the device generated for the purposes of granting and maintaining access and connection to the service. The initial Commission''s proposal (COM-DCD) included a provision that extended the scope of the Directive to cases where the consumer actively provides, in exchange for digital content, counter-performance other than money in the form of personal data or any other data. 94 It follows that data which qualify as ''metadata'' will trigger protection only if the exchange of such data against digital content/services is specifically recognised under domestic law as a 88 COM-DCD, recital 14: ''As regards digital content supplied not in exchange for a price but against counter-performance other than money, this Directive should apply only to contracts where the supplier requests and the consumer actively provides data'' (emphasis added).

abstract:
  This chapter analyses recent developments in the area of digital consumer law in the EU while focusing on the ‘data as counter-performance’ quandary and its application to location data. The immense technological and economic significance of location data in smart urban spaces renders them a relevant subject for inquiry in the context of ongoing legal efforts to protect consumers who grant permission to use their location data in exchange for digital goods and services.


id: cord-033721-o1c7m9wy
title: Semantic Description of Data Mining Datasets: An Ontology-Based Annotation Schema
author: Kostovska, Ana
date: 2020-09-19
txt: ./txt/cord-033721-o1c7m9wy.txt
cache: ./cache/cord-033721-o1c7m9wy.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7556383/
doi: 10.1007/978-3-030-61527-7_10
summary:
  To semantically describe a DM dataset, we consider three different types of vocabularies/ontologies: (1) vocabularies for annotation of provenance information, such as title, description, license, and format; (2) ontologies for annotation of datasets with DM-specific characteristics, i.e., data mining task, datatypes, and dataset specification; and (3) ontologies for annotation of domain-specific knowledge that helps to contextualize the data originating from a given domain. After describing the four characteristics that govern the modeling of the taxonomies of datatypes, data specification, and tasks, we provide an illustrative example that shows how we can combine them in a single annotation schema for the purpose of semantic annotation of DM datasets. To represent the MTR task and MTR dataset specification, we use the classes defined in OntoDM-core, and connect them with the corresponding datatype class from OntoDT (in our case OntoDT: feature-based completely labeled data with record of numeric ordered primitive output) (see Fig. 7 b) .

abstract:
  With the pervasiveness of data mining (DM) in many areas of our society, the management of digital data, readily available for analysis, has become increasingly important. Consequently, nearly all community accepted guidelines and principles (e.g. FAIR and TRUST) for publishing such data in the digital ecosystem, stress the importance of semantic data enhancement. Having rich semantic annotation of DM datasets would support the data mining process at various choice points, such as data understanding, automatic identification of the analysis task, and reasoning over the obtained results. In this paper, we report on the developments of an ontology-based annotation schema for semantic description of DM datasets. The annotation schema combines three different aspects of semantic annotation, i.e., annotation of provenance, data mining specific, and domain-specific information. We demonstrate the utility of these annotations in two use cases: semantic annotation of remote sensing data and data about neurodegenerative diseases.


id: cord-034545-onj7zpi1
title: Internet of things for healthcare monitoring applications based on RFID clustering scheme
author: Abuelkhail, Abdulrahman
date: 2020-11-03
txt: ./txt/cord-034545-onj7zpi1.txt
cache: ./cache/cord-034545-onj7zpi1.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7607371/
doi: 10.1007/s11276-020-02482-1
summary:
  The mathematical model optimizes the following objective functions: (1) minimizing the total distance between CHs and CMs to improve positioning accuracy; and (2) minimizing the number of clusters which reduces the signal transmission traffic Feature 6 (F-6): two level security is obtained by when a node writes data to its RFID tag, the data is signed with a signature, which is a hash value, the obtained hash is encrypted with a AES 128 bits shared key 

abstract:
  COVID-19 surprised the whole world by its quick and sudden spread. Coronavirus pushes all community sectors: government, industry, academia, and nonprofit organizations to take forward steps to stop and control this pandemic. It is evident that IT-based solutions are urgent. This study is a small step in this direction, where health information is monitored and collected continuously. In this work, we build a network of smart nodes where each node comprises a Radio-Frequency Identification (RFID) tag, reduced function RFID reader (RFRR), and sensors. The smart nodes are grouped in clusters, which are constructed periodically. The RFRR reader of the clusterhead collects data from its members, and once it is close to the primary reader, it conveys its data and so on. This approach reduces the primary RFID reader’s burden by receiving data from the clusterheads only instead of reading every tag when they pass by its vicinity. Besides, this mechanism reduces the channel access congestion; thus, it reduces the interference significantly. Furthermore, to protect the exchanged data from potential attacks, two levels of security algorithms, including an AES 128 bit with hashing, have been implemented. The proposed scheme has been validated via mathematical modeling using Integer programming, simulation, and prototype experimentation. The proposed technique shows low data delivery losses and a significant drop in transmission delay compared to contemporary approaches.


id: cord-035030-ig4nwtmi
title: 10th European Conference on Rare Diseases & Orphan Products (ECRD 2020)
author: nan
date: 2020-11-09
txt: ./txt/cord-035030-ig4nwtmi.txt
cache: ./cache/cord-035030-ig4nwtmi.txt
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mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7649705/
doi: 10.1186/s13023-020-01550-1
abstract: nan
summary:
  Conclusion: With this survey Endo-ERN is provided with a large sample of responses from European patients with a rare endocrine condition, and those patients experience unmet needs in research, though these needs differ between the disease groups. Various factors compound the development of treatments for paediatric rare diseases, including the need for new Clinical Outcome Assessments (COAs), as conventional endpoints such as the 6 Minute Walking Test (6MWT) have been shown to not be applicable in all paediatric age subsets, [3] and therefore may not be useful in elucidating patient capabilities. S18 Background: To help inform cross-national development of genomic care pathways, we worked with families of patients with rare diseases and health professionals from two European genetic services


id: cord-035388-n9hza6vm
title: Federated Learning for Healthcare Informatics
author: Xu, Jie
date: 2020-11-12
txt: ./txt/cord-035388-n9hza6vm.txt
cache: ./cache/cord-035388-n9hza6vm.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7659898/
doi: 10.1007/s41666-020-00082-4
summary:
  This creates a big barrier for developing effective analytical approaches that are generalizable, which need diverse, "big data." Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation. For both provider (e.g., building a model for predicting the hospital readmission risk with patient Electronic Health Records (EHR) [71] ) and consumer (patient)-based applications (e.g., screening atrial fibrillation with electrocardiograms captured by smartwatch [79] ), the sensitive patient data can stay either in local institutions or with individual consumers without going out during the federated model learning process, which effectively protects the patient privacy. Federated learning is a problem of training a high-quality shared global model with a central server from decentralized data scattered among large number of different clients (Fig. 1) .

abstract:
  With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies, and pharmaceutical industries, among others. This access provides an unprecedented opportunity for data science technologies to derive data-driven insights and improve the quality of care delivery. Healthcare data, however, are usually fragmented and private making it difficult to generate robust results across populations. For example, different hospitals own the electronic health records (EHR) of different patient populations and these records are difficult to share across hospitals because of their sensitive nature. This creates a big barrier for developing effective analytical approaches that are generalizable, which need diverse, “big data.” Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation. The goal of this survey is to provide a review for federated learning technologies, particularly within the biomedical space. In particular, we summarize the general solutions to the statistical challenges, system challenges, and privacy issues in federated learning, and point out the implications and potentials in healthcare.


id: cord-102238-g6dsnhmm
title: Frequency Energy Analysis in Detecting Rolling Bearing Faults
author: Wescoat, Ethan
date: 2020-12-31
url: https://api.elsevier.com/content/article/pii/S2351978920315894
doi: 10.1016/j.promfg.2020.05.137
abstract:
  Abstract Component failure analysis is sometimes difficult to directly detect due to the complexity of an operating system configuration. Raw time series data is not enough in some cases to understand the type of fault or how it is progressing. The conversion of data from the time domain to the frequency domain assists researchers in making a more discernible difference for detecting failures, but depending on the manufacturing equipment type and complexity, there is still a possibility for inaccurate results. This research explores a method of classifying rolling bearing faults utilizing the total energy gathered from the Power Spectral Density (PSD) of a Fast Fourier Transform (FFT). Using a spectrogram over an entire process cycle, the PSD is swept through time and the total energy is computed and plotted over the periodic machine cycle. Comparing with a baseline set of data, classification patterns emerge, giving an indication of the type of fault, when a fault begins and how the fault progresses. There is a separable difference in each type of fault and a measurable change in the distribution of accumulated damage over time. A roller bearing is used as a validating component, due to the known types of faults and their classifications. Traditional methods are used for comparison and the method verified using experimental and industrial applications. Future application is justified for more complex and not so well-understood systems.


id: cord-102490-yvcrv94c
title: The General Law Principles for Protection the Personal Data and their Importance
author: Souza, Jonatas S. de
date: 2020-09-29
txt: ./txt/cord-102490-yvcrv94c.txt
cache: ./cache/cord-102490-yvcrv94c.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2009.14313v1.pdf
doi: 10.5121/csit.2020.101110
summary:
  The purpose of this paper is to emphasize the principles of the General Law on Personal Data Protection, informing real cases of leakage of personal data and thus obtaining an understanding of the importance of gains that meet the interests of Internet users on the subject and its benefits to the entire Brazilian society. On April 23rd, 2014, Law No. 12,965, now known as Marco Civil da Internet [1] , was approved, establishing principles, guarantees, rights, and duties for the use of the Internet in Brazil, and has the guarantee of privacy and protection of personal data, and will only make such data available through a court order. DispÃµe sobre a proteÃ§Ã£o de dados pessoais e altera a Lei nÂº 12.965, de 23 de abril de 2014 (Marco Civil da Internet)

abstract:
  Rapid technological change and globalization have created new challenges when it comes to the protection and processing of personal data. In 2018, Brazil presented a new law that has the proposal to inform how personal data should be collected and treated, to guarantee the security and integrity of the data holder. The purpose of this paper is to emphasize the principles of the General Law on Personal Data Protection, informing real cases of leakage of personal data and thus obtaining an understanding of the importance of gains that meet the interests of Internet users on the subject and its benefits to the entire Brazilian society.


id: cord-102634-0n42h72w
author: Willforss, Jakob
date: 2020-10-22
txt: ./txt/cord-102634-0n42h72w.txt
cache: ./cache/cord-102634-0n42h72w.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.10.22.349944
doi: 10.1101/2020.10.22.349944
summary:
  Use cases are, for example, (1) Biomarker studies where an initial set of candidates is to be validated (2) Time-series experiment where the global expression is inspected, for instance, at different times after infection (3) Multiomics experiments where multiple types of data are produced for the same or similar biological systems and (4) Detailed studies of comparisons between methods or software approaches. We thus investigated how OmicLoupe can be used for direct comparisons of different data types taken from the same set of samples, to reveal features only detected in certain conditions, and common patterns of observed abundance level changes. To study the similarity of the statistical comparisons across the two data types, features with positive abundance change and with low p-values were highlighted in the RNA-seq contrast (by dragging directly in the figure) between CNV high and CNV low to see how these distribute in the corresponding contrast in the proteomics dataset ( Figure 4B ).

title:
  OmicLoupe: Facilitating biological discovery by interactive exploration of multiple omic datasets and statistical comparisons

abstract:
  Visual exploration of gene product behavior across multiple omic datasets can pinpoint technical limitations in data and reveal biological trends. The OmicLoupe software was developed to facilitate such exploration and provides more than 15 interactive cross-dataset visualizations for omic data. It expands visualizations to multiple datasets for quality control, statistical comparisons and overlap and correlation analyses, while allowing for rapid inspection and downloading of selected features. The usage of OmicLoupe is demonstrated in three diverse studies, including an analysis of SARS-CoV-2 infection across omic layers, based on previously published proteomics and transcriptomics studies. OmicLoupe is available at quantitativeproteomics.org/omicloupe


id: cord-102760-5tkdwtc0
author: Zambetti, Michela
date: 2020-12-31
txt: ./txt/cord-102760-5tkdwtc0.txt
cache: ./cache/cord-102760-5tkdwtc0.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S2351978920315961
doi: 10.1016/j.promfg.2020.05.144
summary:
  In this context, solutions mostly result in the development of low-cost retrofit or upgrade kits that allow integrating legacy equipment into Industry 4.0 environment and thus enable digital servitization. This challenge, however, provides the OEMs with an opportunity to create and capture unique value by upgrading and retrofitting the legacy equipment and then provisioning data-driven value-added services for the manufacturers (equipment users) [5] . In section four we put a special focus on the servitization potential and challenges of the OEMs in supporting the Industry 4.0 transition by means of retrofitting legacy equipment and provisioning data-driven services. Given the fact that the existing literature on the upgradability and retrofitting solution towards Industry 4.0 do not include the OEM and the service perspectives at this point, this research investigated OEM''s potential in providing connectivity and data analytics services to the manufacturers of end products.

title:
  Enabling servitization by retrofitting legacy equipment for Industry 4.0 applications: benefits and barriers for OEMs

abstract:
  Abstract In the wake of industry 4.0, many industries have started to pivot towards digital, collaborative, and smart manufacturing systems by connecting their machinery as part of the Internet of Things (IoT). IoT has the potential to provide visibility and improve manufacturing systems through data collection, analysis, and subsequent actions based on insights generated from large amounts of manufacturing data. Even though comparatively newer equipment come readily equipped with embedded sensors and industrial connectivity necessary to connect to the IoT environment, there are many manufacturers (equipment users) who rely on long standing “legacy systems” that offer no or very limited connectivity. In this context, solutions mostly result in the development of low-cost retrofit or upgrade kits that allow integrating legacy equipment into Industry 4.0 environment and thus enable digital servitization. Servitization is a transformation journey that involves firms developing the capabilities they need to provide technical and data-driven services that supplement traditional product offerings. However, retrofitting solutions of legacy equipment rarely involve Original Equipment Manufacturers (OEMs) who may otherwise leverage the opportunity to create and capture unique value by retrofitting and then provisioning data-driven value-added services for the manufacturers. Hence, the primary objective of this paper is to identify and analyze the available literature on retrofitting and upgrading of the legacy equipment for Industry 4.0 integration. In doing so, this study also investigates the potential opportunities and challenges of OEMs in supporting the Industry 4.0 transition of legacy equipment in a servitization context.


id: cord-103310-qtrquuvv
title: Open-source analytics tools for studying the COVID-19 coronavirus outbreak
author: Wu, Tianzhi
date: 2020-02-27
txt: ./txt/cord-103310-qtrquuvv.txt
cache: ./cache/cord-103310-qtrquuvv.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.02.25.20027433
doi: 10.1101/2020.02.25.20027433
summary:
  To provide convenient access to epidemiological data on the coronavirus outbreak, we developed an R package, nCov2019 (https://github.com/GuangchuangYu/nCov2019). Besides detailed real-time statistics, it offers access to three data sources with detailed daily statistics from December 1, 2019, for 43 countries and more than 500 Chinese cities. We also developed a web app (http://www.bcloud.org/e/) with interactive plots and simple time-series forecasts. [3] , our web app enables users to select their regions of interest and check both the historical and real-time data. Generated by the app on February 25, 2020, Figure 2 shows that the total confirmed cases in the provinces outside Hubei are stabilizing, following a similar trend. Interestingly, daily percent changes in both confirmed cases and deaths in China are decreasing linearly except for a few outliers (see Figure 16 and 18 in Supplementary Document 2).

abstract:
  To provide convenient access to epidemiological data on the coronavirus outbreak, we developed an R package, nCov2019 (https://github.com/GuangchuangYu/nCov2019). Besides detailed real-time statistics, it offers access to three data sources with detailed daily statistics from December 1, 2019, for 43 countries and more than 500 Chinese cities. We also developed a web app (http://www.bcloud.org/e/) with interactive plots and simple time-series forecasts. These analytics tools could be useful in informing the public and studying how this and similar viruses spread in populous countries.


id: cord-103813-w2sb6h94
title: Genetic information insecurity as state of the art
author: Schumacher, Garrett J.
date: 2020-07-10
txt: ./txt/cord-103813-w2sb6h94.txt
cache: ./cache/cord-103813-w2sb6h94.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.07.08.192666
doi: 10.1101/2020.07.08.192666
summary:
  Therefore, human genetic information is a uniquely confidential form of data that requires increased security controls and scrutiny. Sensitive genetic information, which includes both biological material and digital genetic data, is the primary asset of concern, and associated assets, such as metadata, electronic health records and intellectual property, are also vulnerable within this ecosystem. ❖ Private Sensitive Genetic Information can be expected to cause a moderate level of risk to a nation, ethnic group, individual, or stakeholder if it is disclosed, modified, or destroyed without authorization. The genetic information ecosystem is a distributed cyber-physical system containing numerous stakeholders (Supplementary Material, Appendix 1), personnel, and devices for computing and networking purposes. Genetic information security is a shared responsibility between sequencing laboratories and device vendors, as well as all other involved stakeholders. Examples include biorepositories, DNA sequencing laboratories, researchers, cloud and other service providers, and supply chain entities responsible for devices, software and materials.

abstract:
  Genetic information is being generated at an increasingly rapid pace, offering advances in science and medicine that are paralleled only by the threats and risk present within the responsible ecosystem. Human genetic information is identifiable and contains sensitive information, but genetic data security is only recently gaining attention. Genetic data is generated in an evolving and distributed cyber-physical ecosystem, with multiple systems that handle data and multiple partners that utilize the data. This paper defines security classifications of genetic information and discusses the threats, vulnerabilities, and risk found throughout the entire genetic information ecosystem. Laboratory security was found to be especially challenging, primarily due to devices and protocols that were not designed with security in mind. Likewise, other industry standards and best practices threaten the security of the ecosystem. A breach or exposure anywhere in the ecosystem can compromise sensitive information. Extensive development will be required to realize the potential of this emerging field while protecting the bioeconomy and all of its stakeholders.


id: cord-104486-syirijql
title: Data-driven modeling for different stages of pandemic response
author: Adiga, Aniruddha
date: 2020-09-21
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7523119/
doi: nan
abstract:
  Some of the key questions of interest during the COVID-19 pandemic (and all outbreaks) include: where did the disease start, how is it spreading, who is at risk, and how to control the spread. There are a large number of complex factors driving the spread of pandemics, and, as a result, multiple modeling techniques play an increasingly important role in shaping public policy and decision making. As different countries and regions go through phases of the pandemic, the questions and data availability also changes. Especially of interest is aligning model development and data collection to support response efforts at each stage of the pandemic. The COVID-19 pandemic has been unprecedented in terms of real-time collection and dissemination of a number of diverse datasets, ranging from disease outcomes, to mobility, behaviors, and socio-economic factors. The data sets have been critical from the perspective of disease modeling and analytics to support policymakers in real-time. In this overview article, we survey the data landscape around COVID-19, with a focus on how such datasets have aided modeling and response through different stages so far in the pandemic. We also discuss some of the current challenges and the needs that will arise as we plan our way out of the pandemic.


id: cord-130507-baheh8i5
title: Tracking COVID-19 by Tracking Infectious Trajectories
author: Benreguia, Badreddine
date: 2020-05-12
url: https://arxiv.org/pdf/2005.05523v1.pdf
doi: nan
abstract:
  Nowadays, the coronavirus pandemic has and is still causing large numbers of deaths and infected people. Although governments all over the world have taken severe measurements to slow down the virus spreading (e.g., travel restrictions, suspending all sportive, social, and economic activities, quarantines, social distancing, etc.), a lot of persons have died and a lot more are still in danger. Indeed, a recently conducted study~cite{ref2} has reported that 79% of the confirmed infections in China were caused by undocumented patients who had no symptoms. In the same context, in numerous other countries, since coronavirus takes several days before the emergence of symptoms, it has also been reported that the known number of infections is not representative of the real number of infected people (the actual number is expected to be much higher). That is to say, asymptomatic patients are the main factor behind the large quick spreading of coronavirus and are also the major reason that caused governments to lose control over this critical situation. To contribute to remedying this global pandemic, in this paper, we propose an IoT (Internet of Things) investigation system that was specifically designed to spot both undocumented patients and infectious places. The goal is to help the authorities to disinfect high-contamination sites and confine persons even if they have no apparent symptoms. The proposed system also allows determining all persons who had close contact with infected or suspected patients. Consequently, rapid isolation of suspicious cases and more efficient control over any pandemic propagation can be achieved.


id: cord-131678-rvg1ayp2
author: Ponce, Marcelo
date: 2020-09-02
txt: ./txt/cord-131678-rvg1ayp2.txt
cache: ./cache/cord-131678-rvg1ayp2.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2009.01091v1.pdf
doi: nan
summary:
  This paper is organized as follow: in Sec. 2 we describe the covid19.analytics , in Sec. 3 we present some examples of data analysis and visualization, in Sec. 4 we describe in detail how to deploy a web dashboard employing the capabilities of the covid19.analytics package providing full details on the implementation so that this procedure can be repeated and followed by interested users in developing their own dashboards. As the amount of data available for the recorded cases of CoViD19 can be overwhelming, and in order to get a quick insight on the main statistical indicators, the covid19.analytics package includes the report.summary function, which will generate an overall report summarizing the main statistical estimators for the different datasets. The covid19.analytics package provides three different functions to visualize the trends in daily changes of reported cases from time series data.

title:
  covid19.analytics: An R Package to Obtain, Analyze and Visualize Data from the Corona Virus Disease Pandemic

abstract:
  With the emergence of a new pandemic worldwide, a novel strategy to approach it has emerged. Several initiatives under the umbrella of"open science"are contributing to tackle this unprecedented situation. In particular, the"R Language and Environment for Statistical Computing"offers an excellent tool and ecosystem for approaches focusing on open science and reproducible results. Hence it is not surprising that with the onset of the pandemic, a large number of R packages and resources were made available for researches working in the pandemic. In this paper, we present an R package that allows users to access and analyze worldwide data from resources publicly available. We will introduce the covid19.analytics package, focusing in its capabilities and presenting a particular study case where we describe how to deploy the"COVID19.ANALYTICS Dashboard Explorer".


id: cord-133273-kvyzuayp
author: Christ, Andreas
date: 2020-10-05
txt: ./txt/cord-133273-kvyzuayp.txt
cache: ./cache/cord-133273-kvyzuayp.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2010.16241v1.pdf
doi: nan
summary:
  During the literature review it was evident the presence of few works dedicated to evaluating comprehensively the complete cycle of biofeedback, which comprises using the wearable devices, applying Machine Learning patterns detection algorithms, generate the psychologic intervention, besides monitoring its effects and recording the history of events [9, 3] . This solution is being proposed by several literature study about stress patterns and physiological aspects but with few results, for this reason, our project will address topics like experimental study protocol on signals acquisition from patients/participants with wearables to data acquisition and processing, in sequence will be applied machine learning modeling and prediction on biosignal data regarding stress (Fig. 1) . We will present first results of the project concerning a new process model for cooperating data scientists and quality engineers, a product testing model as knowledge base for machine learning computing and visual support of quality engineers in order to explain prediction results.

title:
  Artificial Intelligence: Research Impact on Key Industries; the Upper-Rhine Artificial Intelligence Symposium (UR-AI 2020)

abstract:
  The TriRhenaTech alliance presents a collection of accepted papers of the cancelled tri-national 'Upper-Rhine Artificial Inteeligence Symposium' planned for 13th May 2020 in Karlsruhe. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes 'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.


id: cord-137263-mbww0yyt
author: Hayashi, Teruaki
date: 2020-09-08
txt: ./txt/cord-137263-mbww0yyt.txt
cache: ./cache/cord-137263-mbww0yyt.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2009.04035v1.pdf
doi: nan
summary:
  Using TEEDA, we collect data items (data requests and providable data) in the corona-related confusion in the workshop, discuss the characteristics of missing data, and create three scenarios for data design of unobserved events focusing on variables. In this study, this item will be useful for understanding what types of data and variables are needed and for what purpose in regard to corona-related confusion. The aim of the experiment was to understand the characteristics of data requests and providable data in the corona-related confusion and create scenarios for new data design of unobserved events focusing on variables. Subsequently, participants input the information on the data requests and the providable data about corona-related confusion on TEEDA for 45 min via discussion with other participants. In this study, to discuss the data design of unobserved events in corona-related confusion, we used TEEDA to externalize the information about data items from data users and data providers and analyzed their characteristics.

title:
  Data Requests and Scenarios for Data Design of Unobserved Events in Corona-related Confusion Using TEEDA

abstract:
  Due to the global violence of the novel coronavirus, various industries have been affected and the breakdown between systems has been apparent. To understand and overcome the phenomenon related to this unprecedented crisis caused by the coronavirus infectious disease (COVID-19), the importance of data exchange and sharing across fields has gained social attention. In this study, we use the interactive platform called treasuring every encounter of data affairs (TEEDA) to externalize data requests from data users, which is a tool to exchange not only the information on data that can be provided but also the call for data, what data users want and for what purpose. Further, we analyze the characteristics of missing data in the corona-related confusion stemming from both the data requests and the providable data obtained in the workshop. We also create three scenarios for the data design of unobserved events focusing on variables.


id: cord-138627-jtyoojte
title: Public Goods From Private Data -- An Efficacy and Justification Paradox for Digital Contact Tracing
author: Buzzell, Andrew
date: 2020-07-14
txt: ./txt/cord-138627-jtyoojte.txt
cache: ./cache/cord-138627-jtyoojte.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2007.07016v1.pdf
doi: nan
summary:
  Privacy-centric analysis treats data as private property, frames the relationship between individuals and governments as adversarial, entrenches technology platforms as gatekeepers, and supports a conception of emergency public health authority as limited by individual consent and considerable corporate influence that is in some tension with the more communitarian values that typically inform public health ethics. They require populations be persuaded to use the DCT app, and that hardware and software vendors cooperate with public health authorities to resolve barriers to adoption and usage, such as the need for software modifications to enable passive RSSI measurement. The privacy preserving model serves vendor interests, allowing them to cooperate with public health authorities, thus avoiding regulatory or coercive measures, by limiting the possibility that the use of DCT apps breaks tacit or contractual agreements with their users that could damage already wavering public trust.

abstract:
  Debate about the adoption of digital contact tracing (DCT) apps to control the spread of COVID-19 has focussed on risks to individual privacy (Sharma&Bashir 2020, Tang 2020). This emphasis reveals significant challenges to ethical deployment of DCT, but generates constraints which undermine justification to implement DCT. It would be a mistake to view this result solely as the successful operation of ethical foresight analysis (Floridi&Strait 2020), preventing deployment of potentially harmful technology. Privacy-centric analysis treats data as private property, frames the relationship between individuals and governments as adversarial, entrenches technology platforms as gatekeepers, and supports a conception of emergency public health authority as limited by individual consent and considerable corporate influence that is in some tension with the more communitarian values that typically inform public health ethics. To overcome the barriers to ethical and effective DCT, and develop infrastructure and policy that supports the realization of potential public benefits of digital technology, a public resource conception of aggregate data should be developed.


id: cord-144221-ohorip57
title: Authoritarian Governments Appear to Manipulate COVID Data
author: Kapoor, Mudit
date: 2020-07-19
txt: ./txt/cord-144221-ohorip57.txt
cache: ./cache/cord-144221-ohorip57.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2007.09566v1.pdf
doi: nan
summary:
  First, data on COVID-19 cases and deaths from authoritarian governments show significantly less variation from a 7 day moving average. Second, data on COVID-19 deaths from authoritarian governments do not follow Benford''s law, which describes the distribution of leading digits of numbers. Figure 2 plots the natural logarithm of the mean of the squared deviation of daily cases and deaths per million people, respectively, from the 7 day moving average against the EIU''s overall democracy index score. We investigate whether governments manipulate data by testing whether the COVID-19 data on cumulative cases and deaths across different regimes (authoritarian, hybrid, flawed democracy, and full democracy) confirms to Benford''s law. Natural logarithm of the Mean of squared deviations of observed daily cases and deaths per million people from a 7-day centered moving average, by EIU democracy index score.

abstract:
  Because SARS-Cov-2 (COVID-19) statistics affect economic policies and political outcomes, governments have an incentive to control them. Manipulation may be less likely in democracies, which have checks to ensure transparency. We show that data on disease burden bear indicia of data modification by authoritarian governments relative to democratic governments. First, data on COVID-19 cases and deaths from authoritarian governments show significantly less variation from a 7 day moving average. Because governments have no reason to add noise to data, lower deviation is evidence that data may be massaged. Second, data on COVID-19 deaths from authoritarian governments do not follow Benford's law, which describes the distribution of leading digits of numbers. Deviations from this law are used to test for accounting fraud. Smoothing and adjustments to COVID-19 data may indicate other alterations to these data and a need to account for such alterations when tracking the disease.


id: cord-145831-ag0xt2nj
title: Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering Tasks
author: Schmidt, Lena
date: 2020-01-30
url: https://arxiv.org/pdf/2001.11268v1.pdf
doi: nan
abstract:
  This research on data extraction methods applies recent advances in natural language processing to evidence synthesis based on medical texts. Texts of interest include abstracts of clinical trials in English and in multilingual contexts. The main focus is on information characterized via the Population, Intervention, Comparator, and Outcome (PICO) framework, but data extraction is not limited to these fields. Recent neural network architectures based on transformers show capacities for transfer learning and increased performance on downstream natural language processing tasks such as universal reading comprehension, brought forward by this architecture's use of contextualized word embeddings and self-attention mechanisms. This paper contributes to solving problems related to ambiguity in PICO sentence prediction tasks, as well as highlighting how annotations for training named entity recognition systems are used to train a high-performing, but nevertheless flexible architecture for question answering in systematic review automation. Additionally, it demonstrates how the problem of insufficient amounts of training annotations for PICO entity extraction is tackled by augmentation. All models in this paper were created with the aim to support systematic review (semi)automation. They achieve high F1 scores, and demonstrate the feasibility of applying transformer-based classification methods to support data mining in the biomedical literature.


id: cord-146850-5x6qs2i4
title: The State of AI Ethics Report (June 2020)
author: Gupta, Abhishek
date: 2020-06-25
txt: ./txt/cord-146850-5x6qs2i4.txt
cache: ./cache/cord-146850-5x6qs2i4.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2006.14662v1.pdf
doi: nan
summary:
  Another point brought up in the article is that social media companies might themselves be unwilling to tolerate scraping of their users'' data to do this sort of vetting which against their terms of use for access to the APIs. Borrowing from the credit reporting world, the Fair Credit Reporting Act in the US offers some insights when it mentions that people need to be provided with a recourse to correct information that is used about them in making a decision and that due consent needs to be obtained prior to utilizing such tools to do a background check. Given that AI systems operate in a larger socio-technical ecosystem, we need to tap into fields like law and policy making to come up with effective ways of integrating ethics into AI systems, part of which can involve creating binding legal agreements that tie in with economic incentives.While policy making and law are often seen as slow to adapt to fast changing technology, there are a variety of benefits to be had, for example higher customer trust for services that have adherence to stringent regulations regarding privacy and data protection.

abstract:
  These past few months have been especially challenging, and the deployment of technology in ways hitherto untested at an unrivalled pace has left the internet and technology watchers aghast. Artificial intelligence has become the byword for technological progress and is being used in everything from helping us combat the COVID-19 pandemic to nudging our attention in different directions as we all spend increasingly larger amounts of time online. It has never been more important that we keep a sharp eye out on the development of this field and how it is shaping our society and interactions with each other. With this inaugural edition of the State of AI Ethics we hope to bring forward the most important developments that caught our attention at the Montreal AI Ethics Institute this past quarter. Our goal is to help you navigate this ever-evolving field swiftly and allow you and your organization to make informed decisions. This pulse-check for the state of discourse, research, and development is geared towards researchers and practitioners alike who are making decisions on behalf of their organizations in considering the societal impacts of AI-enabled solutions. We cover a wide set of areas in this report spanning Agency and Responsibility, Security and Risk, Disinformation, Jobs and Labor, the Future of AI Ethics, and more. Our staff has worked tirelessly over the past quarter surfacing signal from the noise so that you are equipped with the right tools and knowledge to confidently tread this complex yet consequential domain.


id: cord-148109-ql1tthyr
title: E-Quarantine: A Smart Health System for Monitoring Coronavirus Patients for Remotely Quarantine
author: El-Din, Doaa Mohey
date: 2020-05-05
url: https://arxiv.org/pdf/2005.04187v1.pdf
doi: nan
abstract:
  Coronavirus becomes officially a global pandemic due to the speed spreading off in various countries. An increasing number of infected with this disease causes the Inability problem to fully care in hospitals and afflict many doctors and nurses inside the hospitals. This paper proposes a smart health system that monitors the patients holding the Coronavirus remotely. Due to protect the lives of the health services members (like physicians and nurses) from infection. This smart system observes the people with this disease based on putting many sensors to record many features of their patients in every second. These parameters include measuring the patient's temperature, respiratory rate, pulse rate, blood pressure, and time. The proposed system saves lives and improves making decisions in dangerous cases. It proposes using artificial intelligence and Internet-of-things to make remotely quarantine and develop decisions in various situations. It provides monitoring patients remotely and guarantees giving patients medicines and getting complete health care without anyone getting sick with this disease. It targets two people's slides the most serious medical conditions and infection and the lowest serious medical conditions in their houses. Observing in hospitals for the most serious medical cases that cause infection in thousands of healthcare members so there is a big need to uses it. Other less serious patients slide, this system enables physicians to monitor patients and get the healthcare from patient's houses to save places for the critical cases in hospitals.


id: cord-154587-qbmm5st9
author: Nguyen, Thanh Thi
date: 2020-07-30
url: https://arxiv.org/pdf/2008.07343v1.pdf
doi: nan
title:
  Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions

abstract:
  Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.


id: cord-159103-dbgs2ado
title: The Future of Digital Health with Federated Learning
author: Rieke, Nicola
date: 2020-03-18
txt: ./txt/cord-159103-dbgs2ado.txt
cache: ./cache/cord-159103-dbgs2ado.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2003.08119v1.pdf
doi: nan
summary:
  The medical FL use-case is inherently different from other domains, e.g. in terms of number of participants and data diversity, and while recent surveys investigate the research advances and open questions of FL [14, 11, 15] , we focus on what it actually means for digital health and what is needed to enable it. Transfer Learning, for example, is a well-established approach of model-sharing that makes it possible to tackle problems with deep neural networks that have millions of parameters, despite the lack of extensive, local datasets that are required for training from scratch: a model is first trained on a large dataset and then further optimised on the actual target data. To adopt this approach into a form of collaborative learning in a FL setup with continuous learning from different institutions, the participants can share their model with a peer-to-peer architecture in a "round-robin" or parallel fashion and train in turn on their local data.

abstract:
  Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how Federated Learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.


id: cord-160526-27kmder5
title: Statistical Issues and Recommendations for Clinical Trials Conducted During the COVID-19 Pandemic
author: Meyer, R. Daniel
date: 2020-05-21
url: https://arxiv.org/pdf/2005.10248v1.pdf
doi: nan
abstract:
  The COVID-19 pandemic has had and continues to have major impacts on planned and ongoing clinical trials. Its effects on trial data create multiple potential statistical issues. The scale of impact is unprecedented, but when viewed individually, many of the issues are well defined and feasible to address. A number of strategies and recommendations are put forward to assess and address issues related to estimands, missing data, validity and modifications of statistical analysis methods, need for additional analyses, ability to meet objectives and overall trial interpretability.


id: cord-162326-z7ta3pp9
title: AMUSED: An Annotation Framework of Multi-modal Social Media Data
author: Shahi, Gautam Kishore
date: 2020-10-01
txt: ./txt/cord-162326-z7ta3pp9.txt
cache: ./cache/cord-162326-z7ta3pp9.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2010.00502v1.pdf
doi: nan
summary:
  AMUSED can be applied in multiple application domains, as a use case, we have implemented the framework for collecting COVID-19 misinformation data from different social media platforms. To present a use case, we apply the proposed framework to gather data on COVID-19 misinformation on multiple social media platforms. In the following sections, we discuss the related work, different types of data circulated and its restrictions on social media platforms, current annotation techniques, proposed methodology and possible application domain; then we discuss the implementation and result. Nowadays, the journalists cover some of the common issues like misinformation, mob lynching, hate speech, and they also link the social media post in the news articles Cui and Liu (2017) . Step 5: Social Media Link From the crawled data, we fetch the anchor tag( a ) mentioned in the news content, then we filter the hyperlinks to identify social media platforms like Twitter and YouTube.

abstract:
  In this paper, we present a semi-automated framework called AMUSED for gathering multi-modal annotated data from the multiple social media platforms. The framework is designed to mitigate the issues of collecting and annotating social media data by cohesively combining machine and human in the data collection process. From a given list of the articles from professional news media or blog, AMUSED detects links to the social media posts from news articles and then downloads contents of the same post from the respective social media platform to gather details about that specific post. The framework is capable of fetching the annotated data from multiple platforms like Twitter, YouTube, Reddit. The framework aims to reduce the workload and problems behind the data annotation from the social media platforms. AMUSED can be applied in multiple application domains, as a use case, we have implemented the framework for collecting COVID-19 misinformation data from different social media platforms.


id: cord-169484-mjtlhh5e
title: Dashboard of sentiment in Austrian social media during COVID-19
author: Pellert, Max
date: 2020-06-19
txt: ./txt/cord-169484-mjtlhh5e.txt
cache: ./cache/cord-169484-mjtlhh5e.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2006.11158v1.pdf
doi: nan
summary:
  To track online emotional expressions of the Austrian population close to real-time during the COVID-19 pandemic, we build a self-updating monitor of emotion dynamics using digital traces from three different data sources. The interactive dashboard showcasing our data is available online under http://www.mpellert.at/covid19_monitor_austria/. We gather these data in the form of text from platforms such as Twitter and news forums, where large groups of users discuss timely issues. To fill a gap, we build a dashboard with processed data from three different sources to track the sentiment in Austrian social media during COVID-19. In addition, measures that strongly affect people''s daily lives over a long period of time, as well as high level of uncertainty, likely contribute to the unprecedented changes of collective emotional expression in online social media.

abstract:
  To track online emotional expressions of the Austrian population close to real-time during the COVID-19 pandemic, we build a self-updating monitor of emotion dynamics using digital traces from three different data sources. This enables decision makers and the interested public to assess issues such as the attitude towards counter-measures taken during the pandemic and the possible emergence of a (mental) health crisis early on. We use web scraping and API access to retrieve data from the news platform derstandard.at, Twitter and a chat platform for students. We document the technical details of our workflow in order to provide materials for other researchers interested in building a similar tool for different contexts. Automated text analysis allows us to highlight changes of language use during COVID-19 in comparison to a neutral baseline. We use special word clouds to visualize that overall difference. Longitudinally, our time series show spikes in anxiety that can be linked to several events and media reporting. Additionally, we find a marked decrease in anger. The changes last for remarkably long periods of time (up to 12 weeks). We discuss these and more patterns and connect them to the emergence of collective emotions. The interactive dashboard showcasing our data is available online under http://www.mpellert.at/covid19_monitor_austria/. Our work has attracted media attention and is part of an web archive of resources on COVID-19 collected by the Austrian National Library.


id: cord-176472-4sx34j90
author: Diou, Christos
date: 2020-05-06
url: https://arxiv.org/pdf/2005.02928v1.pdf
doi: nan
title:
  BigO: A public health decision support system for measuring obesogenic behaviors of children in relation to their local environment

abstract:
  Obesity is a complex disease and its prevalence depends on multiple factors related to the local socioeconomic, cultural and urban context of individuals. Many obesity prevention strategies and policies, however, are horizontal measures that do not depend on context-specific evidence. In this paper we present an overview of BigO (http://bigoprogram.eu), a system designed to collect objective behavioral data from children and adolescent populations as well as their environment in order to support public health authorities in formulating effective, context-specific policies and interventions addressing childhood obesity. We present an overview of the data acquisition, indicator extraction, data exploration and analysis components of the BigO system, as well as an account of its preliminary pilot application in 33 schools and 2 clinics in four European countries, involving over 4,200 participants.


id: cord-176677-exej3zwh
title: When we can trust computers (and when we can't)
author: Coveney, Peter V.
date: 2020-07-08
url: https://arxiv.org/pdf/2007.03741v1.pdf
doi: nan
abstract:
  With the relentless rise of computer power, there is a widespread expectation that computers can solve the most pressing problems of science, and even more besides. We explore the limits of computational modelling and conclude that, in the domains of science and engineering that are relatively simple and firmly grounded in theory, these methods are indeed powerful. Even so, the availability of code, data and documentation, along with a range of techniques for validation, verification and uncertainty quantification, are essential for building trust in computer generated findings. When it comes to complex systems in domains of science that are less firmly grounded in theory, notably biology and medicine, to say nothing of the social sciences and humanities, computers can create the illusion of objectivity, not least because the rise of big data and machine learning pose new challenges to reproducibility, while lacking true explanatory power. We also discuss important aspects of the natural world which cannot be solved by digital means. In the long-term, renewed emphasis on analogue methods will be necessary to temper the excessive faith currently placed in digital computation.


id: cord-183016-ajwnihk6
title: Containing Future Epidemics with Trustworthy Federated Systems for Ubiquitous Warning and Response
author: Carrillo, Dick
date: 2020-10-26
txt: ./txt/cord-183016-ajwnihk6.txt
cache: ./cache/cord-183016-ajwnihk6.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2010.13392v1.pdf
doi: nan
summary:
  In this context, one main factor is to design a special set of incentives that would allow the citizens to provide secured anonymized access to their data while actively participating in the crowd platform to support early disease detection, a public information system, and possible mitigation measures. 2) a federated global epidemiological warning system is proposed based on DLTs. 3) a proof of concept of the integration between DLT and NB-IoT is used to evaluate the wireless network performance on the IoT infrastructure supporting a remote patient monitoring use case. There are three principal sources of epidemic-relevant data acquired through wireless connectivity: (1) online social networks; (2) personal smart phone and mobile data; and (3) sensory and Internet of Things (IoT) devices. In the context of the proposed federated global epidemiological warning system, the remote patient monitoring is a representative use case, in which the integration between DLTs and IoT devices plays a key role.

abstract:
  In this paper, we propose a global digital platform to avoid and combat epidemics by providing relevant real-time information to support selective lockdowns. It leverages the pervasiveness of wireless connectivity while being trustworthy and secure. The proposed system is conceptualized to be decentralized yet federated, based on ubiquitous public systems and active citizen participation. Its foundations lie on the principle of informational self-determination. We argue that only in this way it can become a trustworthy and legitimate public good infrastructure for citizens by balancing the asymmetry of the different hierarchical levels within the federated organization while providing highly effective detection and guiding mitigation measures towards graceful lockdown of the society. To exemplify the proposed system, we choose the remote patient monitoring as use case. In which, the integration of distributed ledger technologies with narrowband IoT technology is evaluated considering different number of endorsed peers. An experimental proof of concept setup is used to evaluate the performance of this integration, in which the end-to-end latency is slightly increased when a new endorsed element is added. However, the system reliability, privacy, and interoperability are guaranteed. In this sense, we expect active participation of empowered citizens to supplement the more usual top-down management of epidemics.


id: cord-184194-zdxebonv
title: Using Deep Learning and Explainable Artificial Intelligence in Patients' Choices of Hospital Levels
author: Chen, Lichin
date: 2020-06-24
url: https://arxiv.org/pdf/2006.13427v1.pdf
doi: nan
abstract:
  In countries that enabled patients to choose their own providers, a common problem is that the patients did not make rational decisions, and hence, fail to use healthcare resources efficiently. This might cause problems such as overwhelming tertiary facilities with mild condition patients, thus limiting their capacity of treating acute and critical patients. To address such maldistributed patient volume, it is essential to oversee patients choices before further evaluation of a policy or resource allocation. This study used nationwide insurance data, accumulated possible features discussed in existing literature, and used a deep neural network to predict the patients choices of hospital levels. This study also used explainable artificial intelligence methods to interpret the contribution of features for the general public and individuals. In addition, we explored the effectiveness of changing data representations. The results showed that the model was able to predict with high area under the receiver operating characteristics curve (AUC) (0.90), accuracy (0.90), sensitivity (0.94), and specificity (0.97) with highly imbalanced label. Generally, social approval of the provider by the general public (positive or negative) and the number of practicing physicians serving per ten thousand people of the located area are listed as the top effecting features. The changing data representation had a positive effect on the prediction improvement. Deep learning methods can process highly imbalanced data and achieve high accuracy. The effecting features affect the general public and individuals differently. Addressing the sparsity and discrete nature of insurance data leads to better prediction. Applications using deep learning technology are promising in health policy making. More work is required to interpret models and practice implementation.


id: cord-185121-f6vjm4j4
title: A computational tool for trend analysis and forecast of the COVID-19 pandemic
author: Paiva, Henrique Mohallem
date: 2020-10-20
txt: ./txt/cord-185121-f6vjm4j4.txt
cache: ./cache/cord-185121-f6vjm4j4.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2010.10332v1.pdf
doi: nan
summary:
  Country-wise data from the European Centre for Disease Prevention and Control (ECDC) concerning the daily number of cases and demises around the world are used, as well as detailed data from Johns Hopkins University and from the Brasil.io project describing individually the occurrences in United States counties and in Brazilian states and cities, respectively. Conclusion: The main contributions of this work lie in (i) a straightforward model of the curves to represent the data, which allows automation of the process without requiring interventions from experts; (ii) an innovative approach for trend analysis, whose results provide important information to support authorities in their decision-making process; and (iii) the developed computational tool, which is freely available and allows the user to quickly update the COVID-19 analyses and forecasts for any country, United States county or Brazilian state or city present in the periodic reports from the authorities.

abstract:
  Purpose: This paper proposes a methodology and a computational tool to study the COVID-19 pandemic throughout the world and to perform a trend analysis to assess its local dynamics. Methods: Mathematical functions are employed to describe the number of cases and demises in each region and to predict their final numbers, as well as the dates of maximum daily occurrences and the local stabilization date. The model parameters are calibrated using a computational methodology for numerical optimization. Trend analyses are run, allowing to assess the effects of public policies. Easy to interpret metrics over the quality of the fitted curves are provided. Country-wise data from the European Centre for Disease Prevention and Control (ECDC) concerning the daily number of cases and demises around the world are used, as well as detailed data from Johns Hopkins University and from the Brasil.io project describing individually the occurrences in United States counties and in Brazilian states and cities, respectively. U. S. and Brazil were chosen for a more detailed analysis because they are the current foci of the pandemic. Results: Illustrative results for different countries, U. S. counties and Brazilian states and cities are presented and discussed. Conclusion: The main contributions of this work lie in (i) a straightforward model of the curves to represent the data, which allows automation of the process without requiring interventions from experts; (ii) an innovative approach for trend analysis, whose results provide important information to support authorities in their decision-making process; and (iii) the developed computational tool, which is freely available and allows the user to quickly update the COVID-19 analyses and forecasts for any country, United States county or Brazilian state or city present in the periodic reports from the authorities.


id: cord-197127-o30tiqel
title: Numerical differentiation of noisy data: A unifying multi-objective optimization framework
author: Breugel, Floris van
date: 2020-09-03
txt: ./txt/cord-197127-o30tiqel.txt
cache: ./cache/cord-197127-o30tiqel.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2009.01911v2.pdf
doi: nan
summary:
  In this work, we take a principled approach and propose a multi-objective optimization framework for choosing parameters that minimize a loss function to balance the faithfulness and smoothness of the derivative estimate. To understand the qualities of the derivative estimates resulting from parameters selected by our loss function, we begin by analyzing the derivative estimates of noisy sinusoidal curves using the Savitzky-Golay filter and return to our original metrics, RMSE and error correlation to evaluate the results. To characterize this relationship, we evaluated the performance of derivative estimates achieved by a Savitzky-Golay filter by sweeping through different values of γ for a suite of sinusoidal data with various frequencies (f ), noise levels (additive white (zero-mean) Gaussian noise with variance σ 2 ), temporal resolutions (∆t), and dataset lengths (in time steps, L) ( Fig. 2A-B) .

abstract:
  Computing derivatives of noisy measurement data is ubiquitous in the physical, engineering, and biological sciences, and it is often a critical step in developing dynamic models or designing control. Unfortunately, the mathematical formulation of numerical differentiation is typically ill-posed, and researchers often resort to an textit{ad hoc} process for choosing one of many computational methods and its parameters. In this work, we take a principled approach and propose a multi-objective optimization framework for choosing parameters that minimize a loss function to balance the faithfulness and smoothness of the derivative estimate. Our framework has three significant advantages. First, the task of selecting multiple parameters is reduced to choosing a single hyper-parameter. Second, where ground-truth data is unknown, we provide a heuristic for automatically selecting this hyper-parameter based on the power spectrum and temporal resolution of the data. Third, the optimal value of the hyper-parameter is consistent across different differentiation methods, thus our approach unifies vastly different numerical differentiation methods and facilitates unbiased comparison of their results. Finally, we provide an extensive open-source Python library texttt{pynumdiff} to facilitate easy application to diverse datasets (https://github.com/florisvb/PyNumDiff).


id: cord-198180-pwmr3m4o
title: Future Smart Connected Communities to Fight COVID-19 Outbreak
author: Gupta, Deepti
date: 2020-07-20
url: https://arxiv.org/pdf/2007.10477v1.pdf
doi: nan
abstract:
  Internet of Things (IoT) has grown rapidly in the last decade and continue to develop in terms of dimension and complexity offering wide range of devices to support diverse set of applications. With ubiquitous Internet, connected sensors and actuators, networking and communication technology, and artificial intelligence (AI), smart cyber-physical systems (CPS) provide services rendering assistance to humans in their daily lives. However, the recent outbreak of COVID-19 (also known as coronavirus) pandemic has exposed and highlighted the limitations of current technological deployments to curtail this disease. IoT and smart connected technologies together with data-driven applications can play a crucial role not only in prevention, continuous monitoring, and mitigation of the disease, but also enable prompt enforcement of guidelines, rules and government orders to contain such future outbreaks. In this paper, we envision an IoT-enabled ecosystem for intelligent monitoring, pro-active prevention and control, and mitigation of COVID-19. We propose different architectures, applications and technology systems for various smart infrastructures including E-health, smart home, smart supply chain management, smart locality, and smart city, to develop future connected communities to manage and mitigate similar outbreaks. Furthermore, we present research challenges together with future directions to enable and develop these smart communities and infrastructures to fight and prepare against such outbreaks.


id: cord-199267-cm6tqbzk
title: Survive the Schema Changes: Integration of Unmanaged Data Using Deep Learning
author: Wang, Zijie
date: 2020-10-15
txt: ./txt/cord-199267-cm6tqbzk.txt
cache: ./cache/cord-199267-cm6tqbzk.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2010.07586v1.pdf
doi: nan
summary:
  In this work, we propose to use deep learning to automatically deal with schema changes through a super cell representation and automatic injection of perturbations to the training data to make the model robust to schema changes. The contributions of this work include: (1) As to our best knowledge, we are the first to systematically investigate the application of deep learning and adversarial training techniques to automatically handle schema changes occurring in the data sources. A deep learning model, once trained, can handle most schema evolution without any human intervention, and does not require any data migration, or version management overhead. Our work has a potential to integrate data discovery and schema matching into a deep learning model inference process.

abstract:
  Data is the king in the age of AI. However data integration is often a laborious task that is hard to automate. Schema change is one significant obstacle to the automation of the end-to-end data integration process. Although there exist mechanisms such as query discovery and schema modification language to handle the problem, these approaches can only work with the assumption that the schema is maintained by a database. However, we observe diversified schema changes in heterogeneous data and open data, most of which has no schema defined. In this work, we propose to use deep learning to automatically deal with schema changes through a super cell representation and automatic injection of perturbations to the training data to make the model robust to schema changes. Our experimental results demonstrate that our proposed approach is effective for two real-world data integration scenarios: coronavirus data integration, and machine log integration.


id: cord-204835-1yay69kq
title: A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data
author: Sun, Chenxi
date: 2020-10-23
txt: ./txt/cord-204835-1yay69kq.txt
cache: ./cache/cord-204835-1yay69kq.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2010.12493v2.pdf
doi: nan
summary:
  title: A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data Irregularly sampled time series (ISTS) data has irregular temporal intervals between observations and different sampling rates between sequences. Recurrent neural networks (RNNs) [25, 26, 27] , auto-encoder (AE) [28, 29] and generative adversarial networks (GANs) [30, 31] have achieved good performance in medical data imputation and medical prediction thanks to their abilities of learning and generalization obtained by complex nonlinearity. End-to-end approaches process the downstream tasks directly based on modeling the time series with missing data. According to the analysis of technologies and experiment results, in this section, we will discuss ISMTS modeling task from three perspectives -1) imputation task with prediction task, 2) intra-series relation with inter-series relation / local structure with global structure and 3) missing data with raw data. Thus, of particular interest are irregularity-based methods that can learn directly by using multivariate sparse and irregularly sampled time series as input without the need for other imputation.

abstract:
  Irregularly sampled time series (ISTS) data has irregular temporal intervals between observations and different sampling rates between sequences. ISTS commonly appears in healthcare, economics, and geoscience. Especially in the medical environment, the widely used Electronic Health Records (EHRs) have abundant typical irregularly sampled medical time series (ISMTS) data. Developing deep learning methods on EHRs data is critical for personalized treatment, precise diagnosis and medical management. However, it is challenging to directly use deep learning models for ISMTS data. On the one hand, ISMTS data has the intra-series and inter-series relations. Both the local and global structures should be considered. On the other hand, methods should consider the trade-off between task accuracy and model complexity and remain generality and interpretability. So far, many existing works have tried to solve the above problems and have achieved good results. In this paper, we review these deep learning methods from the perspectives of technology and task. Under the technology-driven perspective, we summarize them into two categories - missing data-based methods and raw data-based methods. Under the task-driven perspective, we also summarize them into two categories - data imputation-oriented and downstream task-oriented. For each of them, we point out their advantages and disadvantages. Moreover, we implement some representative methods and compare them on four medical datasets with two tasks. Finally, we discuss the challenges and opportunities in this area.


id: cord-206145-snkdgpym
title: Object Recognition for Economic Development from Daytime Satellite Imagery
author: Ackermann, Klaus
date: 2020-09-11
url: https://arxiv.org/pdf/2009.05455v1.pdf
doi: nan
abstract:
  Reliable data about the stock of physical capital and infrastructure in developing countries is typically very scarce. This is particular a problem for data at the subnational level where existing data is often outdated, not consistently measured or coverage is incomplete. Traditional data collection methods are time and labor-intensive costly, which often prohibits developing countries from collecting this type of data. This paper proposes a novel method to extract infrastructure features from high-resolution satellite images. We collected high-resolution satellite images for 5 million 1km $times$ 1km grid cells covering 21 African countries. We contribute to the growing body of literature in this area by training our machine learning algorithm on ground-truth data. We show that our approach strongly improves the predictive accuracy. Our methodology can build the foundation to then predict subnational indicators of economic development for areas where this data is either missing or unreliable.


id: cord-209932-1lsv7cel
title: Predicting financial markets with Google Trends and not so random keywords
author: Challet, Damien
date: 2013-07-17
url: https://arxiv.org/pdf/1307.4643v3.pdf
doi: nan
abstract:
  We check the claims that data from Google Trends contain enough data to predict future financial index returns. We first discuss the many subtle (and less subtle) biases that may affect the backtest of a trading strategy, particularly when based on such data. Expectedly, the choice of keywords is crucial: by using an industry-grade backtesting system, we verify that random finance-related keywords do not to contain more exploitable predictive information than random keywords related to illnesses, classic cars and arcade games. We however show that other keywords applied on suitable assets yield robustly profitable strategies, thereby confirming the intuition of Preis et al. (2013)


id: cord-219107-klpmipaj
title: Risk mapping for COVID-19 outbreaks using mobility data
author: Zachreson, Cameron
date: 2020-08-14
txt: ./txt/cord-219107-klpmipaj.txt
cache: ./cache/cord-219107-klpmipaj.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2008.06193v1.pdf
doi: nan
summary:
  For community transmission scenarios, our results demonstrate that mobility data adds the most value to risk predictions when case counts are low and spatially clustered. In each case, we use the Facebook mobility data that was available during the early stages of the outbreak to estimate future spatial patterns of relative transmission risk. For each of the three outbreak scenarios, we present the mobility-based estimates of the relative transmission risk distribution, and a time-varying correlation between our estimate and the case numbers ascertained through contact tracing and testing programs. Our results indicate that aggregate mobility data can be a useful tool in estimation of COVID-19 transmission risk diffusion from locations where active cases have been identified. A heat map (Supplemental Figure S1 ) of the average number of Facebook users present during the nighttime period (2am to 10am) as a proportion of the estimated resident population reported by the ABS (2018 [32] ) shows qualitative similarity to the spatial distributions of active cases and relative risk shown in Figure 5 

abstract:
  COVID-19 is highly transmissible and containing outbreaks requires a rapid and effective response. Because infection may be spread by people who are pre-symptomatic or asymptomatic, substantial undetected transmission is likely to occur before clinical cases are diagnosed. Thus, when outbreaks occur there is a need to anticipate which populations and locations are at heightened risk of exposure. In this work, we evaluate the utility of aggregate human mobility data for estimating the geographic distribution of transmission risk. We present a simple procedure for producing spatial transmission risk assessments from near-real-time population mobility data. We validate our estimates against three well-documented COVID-19 outbreak scenarios in Australia. Two of these were well-defined transmission clusters and one was a community transmission scenario. Our results indicate that mobility data can be a good predictor of geographic patterns of exposure risk from transmission centres, particularly in scenarios involving workplaces or other environments associated with habitual travel patterns. For community transmission scenarios, our results demonstrate that mobility data adds the most value to risk predictions when case counts are low and spatially clustered. Our method could assist health systems in the allocation of testing resources, and potentially guide the implementation of geographically-targeted restrictions on movement and social interaction.


id: cord-223332-51670qld
title: An operational architecture for privacy-by-design in public service applications
author: Agrawal, Prashant
date: 2020-06-08
txt: ./txt/cord-223332-51670qld.txt
cache: ./cache/cord-223332-51670qld.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2006.04654v1.pdf
doi: nan
summary:
  In this paper, we present an operational architecture for privacy-by-design based on independent regulatory oversight stipulated by most data protection regimes, regulated access control, purpose limitation and data minimisation. an interest in preventing information about the self from being disseminated and controlling the extent of access to information." It would be the role of a future Indian data protection law to create some objective standards for informational privacy to give all actors in society an understanding of the "ground rules" for accessing an individuals'' personal information. The need for early alignment of legal and technical design principles of data systems, such as access controls, purpose limitation and clear liability frameworks under appropriate regulatory jurisdictions are essential to create secure and trustworthy public data infrastructures [5, 6, 7] . We have presented the design sketch of an operational architecture for privacy-by-design [3] based on regulatory oversight, regulated access control, purpose limitation and data minimisation.

abstract:
  Governments around the world are trying to build large data registries for effective delivery of a variety of public services. However, these efforts are often undermined due to serious concerns over privacy risks associated with collection and processing of personally identifiable information. While a rich set of special-purpose privacy-preserving techniques exist in computer science, they are unable to provide end-to-end protection in alignment with legal principles in the absence of an overarching operational architecture to ensure purpose limitation and protection against insider attacks. This either leads to weak privacy protection in large designs, or adoption of overly defensive strategies to protect privacy by compromising on utility. In this paper, we present an operational architecture for privacy-by-design based on independent regulatory oversight stipulated by most data protection regimes, regulated access control, purpose limitation and data minimisation. We briefly discuss the feasibility of implementing our architecture based on existing techniques. We also present some sample case studies of privacy-preserving design sketches of challenging public service applications.


id: cord-224516-t5zubl1p
title: SARS-CoV-2, a Threat to Privacy?
author: Daubenschuetz, Tim
date: 2020-04-21
txt: ./txt/cord-224516-t5zubl1p.txt
cache: ./cache/cord-224516-t5zubl1p.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2004.10305v1.pdf
doi: nan
summary:
  We furthermore discuss the issues with privacy that can occur during a crisis such as this global pandemic and what can be done to ensure information security and hence appropriate data protection. When we are considering the example of doctors treating their patients, we can use the framework of contextual integrity to reason about the appropriate information flow as follows: the patient is both the sender and the subject of the data exchange, the doctor is the receiver, the information type is the patient''s medical information, the transmission principle includes, most importantly, doctor-patient confidentiality aside from public health issues. In Germany, the authority for disease control and prevention, the Robert Koch Institute (RKI), made headlines on March 18, 2020, as it became public that telecommunication provider Telekom had shared an anonymized set of mobile phone movement data to monitor citizens'' mobility in the fight against SARS-CoV-2.

abstract:
  The global SARS-CoV-2 pandemic is currently putting a massive strain on the world's critical infrastructures. With healthcare systems and internet service providers already struggling to provide reliable service, some operators may, intentionally or unintentionally, lever out privacy-protecting measures to increase their system's efficiency in fighting the virus. Moreover, though it may seem all encouraging to see the effectiveness of authoritarian states in battling the crisis, we, the authors of this paper, would like to raise the community's awareness towards developing more effective means in battling the crisis without the need to limit fundamental human rights. To analyze the current situation, we are discussing and evaluating the steps corporations and governments are taking to condemn the virus by applying established privacy research.


id: cord-225826-bwghyhqx
author: Jiang, Zheng
date: 2020-04-15
url: https://arxiv.org/pdf/2004.06912v1.pdf
doi: nan
title:
  Combining Visible Light and Infrared Imaging for Efficient Detection of Respiratory Infections such as COVID-19 on Portable Device

abstract:
  Coronavirus Disease 2019 (COVID-19) has become a serious global epidemic in the past few months and caused huge loss to human society worldwide. For such a large-scale epidemic, early detection and isolation of potential virus carriers is essential to curb the spread of the epidemic. Recent studies have shown that one important feature of COVID-19 is the abnormal respiratory status caused by viral infections. During the epidemic, many people tend to wear masks to reduce the risk of getting sick. Therefore, in this paper, we propose a portable non-contact method to screen the health condition of people wearing masks through analysis of the respiratory characteristics. The device mainly consists of a FLIR one thermal camera and an Android phone. This may help identify those potential patients of COVID-19 under practical scenarios such as pre-inspection in schools and hospitals. In this work, we perform the health screening through the combination of the RGB and thermal videos obtained from the dual-mode camera and deep learning architecture.We first accomplish a respiratory data capture technique for people wearing masks by using face recognition. Then, a bidirectional GRU neural network with attention mechanism is applied to the respiratory data to obtain the health screening result. The results of validation experiments show that our model can identify the health status on respiratory with the accuracy of 83.7% on the real-world dataset. The abnormal respiratory data and part of normal respiratory data are collected from Ruijin Hospital Affiliated to The Shanghai Jiao Tong University Medical School. Other normal respiratory data are obtained from healthy people around our researchers. This work demonstrates that the proposed portable and intelligent health screening device can be used as a pre-scan method for respiratory infections, which may help fight the current COVID-19 epidemic.


id: cord-226263-ns628u21
author: Ye, Yanfang
date: 2020-03-27
url: https://arxiv.org/pdf/2003.12232v1.pdf
doi: nan
title:
  $alpha$-Satellite: An AI-driven System and Benchmark Datasets for Hierarchical Community-level Risk Assessment to Help Combat COVID-19

abstract:
  The novel coronavirus and its deadly outbreak have posed grand challenges to human society: as of March 26, 2020, there have been 85,377 confirmed cases and 1,293 reported deaths in the United States; and the World Health Organization (WHO) characterized coronavirus disease (COVID-19) - which has infected more than 531,000 people with more than 24,000 deaths in at least 171 countries - a global pandemic. A growing number of areas reporting local sub-national community transmission would represent a significant turn for the worse in the battle against the novel coronavirus, which points to an urgent need for expanded surveillance so we can better understand the spread of COVID-19 and thus better respond with actionable strategies for community mitigation. By advancing capabilities of artificial intelligence (AI) and leveraging the large-scale and real-time data generated from heterogeneous sources (e.g., disease related data from official public health organizations, demographic data, mobility data, and user geneated data from social media), in this work, we propose and develop an AI-driven system (named $alpha$-Satellite}, as an initial offering, to provide hierarchical community-level risk assessment to assist with the development of strategies for combating the fast evolving COVID-19 pandemic. More specifically, given a specific location (either user input or automatic positioning), the developed system will automatically provide risk indexes associated with it in a hierarchical manner (e.g., state, county, city, specific location) to enable individuals to select appropriate actions for protection while minimizing disruptions to daily life to the extent possible. The developed system and the generated benchmark datasets have been made publicly accessible through our website. The system description and disclaimer are also available in our website.


id: cord-226956-n5qwsvtr
author: Arbia, Giuseppe
date: 2020-03-13
url: https://arxiv.org/pdf/2003.06207v1.pdf
doi: nan
title:
  A Note on Early Epidemiological Analysis of Coronavirus Disease 2019 Outbreak using Crowdsourced Data

abstract:
  Crowdsourcing data can prove of paramount importance in monitoring and controlling the spread of infectious diseases. The recent paper by Sun, Chen and Viboud (2020) is important because it contributes to the understanding of the epidemiology and of the spreading of Covid-19 in a period when most of the epidemic characteristics are still unknown. However, the use of crowdsourcing data raises a number of problems from the statistical point of view which run the risk of invalidating the results and of biasing estimation and hypothesis testing. While the work by Sun, Chen and Viboud (2020) has to be commended, given the importance of the topic for worldwide health security, in this paper we deem important to remark the presence of the possible sources of statistical biases and to point out possible solutions to them


id: cord-229198-aju7xkel
title: Sketch and Scale: Geo-distributed tSNE and UMAP
author: Wei, Viska
date: 2020-11-11
url: https://arxiv.org/pdf/2011.06103v1.pdf
doi: nan
abstract:
  Running machine learning analytics over geographically distributed datasets is a rapidly arising problem in the world of data management policies ensuring privacy and data security. Visualizing high dimensional data using tools such as t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP) became common practice for data scientists. Both tools scale poorly in time and memory. While recent optimizations showed successful handling of 10,000 data points, scaling beyond million points is still challenging. We introduce a novel framework: Sketch and Scale (SnS). It leverages a Count Sketch data structure to compress the data on the edge nodes, aggregates the reduced size sketches on the master node, and runs vanilla tSNE or UMAP on the summary, representing the densest areas, extracted from the aggregated sketch. We show this technique to be fully parallel, scale linearly in time, logarithmically in memory, and communication, making it possible to analyze datasets with many millions, potentially billions of data points, spread across several data centers around the globe. We demonstrate the power of our method on two mid-size datasets: cancer data with 52 million 35-band pixels from multiple images of tumor biopsies; and astrophysics data of 100 million stars with multi-color photometry from the Sloan Digital Sky Survey (SDSS).


id: cord-233012-ltbvpv8b
title: Global Data Science Project for COVID-19 Summary Report
author: Garcia-Gasulla, Dario
date: 2020-06-10
url: https://arxiv.org/pdf/2006.05573v1.pdf
doi: nan
abstract:
  This paper aims at providing the summary of the Global Data Science Project (GDSC) for COVID-19. as on May 31 2020. COVID-19 has largely impacted on our societies through both direct and indirect effects transmitted by the policy measures to counter the spread of viruses. We quantitatively analysed the multifaceted impacts of the COVID-19 pandemic on our societies including people's mobility, health, and social behaviour changes. People's mobility has changed significantly due to the implementation of travel restriction and quarantine measurements. Indeed, the physical distance has widened at international (cross-border), national and regional level. At international level, due to the travel restrictions, the number of international flights has plunged overall at around 88 percent during March. In particular, the number of flights connecting Europe dropped drastically in mid of March after the United States announced travel restrictions to Europe and the EU and participating countries agreed to close borders, at 84 percent decline compared to March 10th. Similarly, we examined the impacts of quarantine measures in the major city: Tokyo (Japan), New York City (the United States), and Barcelona (Spain). Within all three cities, we found the significant decline in traffic volume. We also identified the increased concern for mental health through the analysis of posts on social networking services such as Twitter and Instagram. Notably, in the beginning of April 2020, the number of post with #depression on Instagram doubled, which might reflect the rise in mental health awareness among Instagram users. Besides, we identified the changes in a wide range of people's social behaviors, as well as economic impacts through the analysis of Instagram data and primary survey data.


id: cord-238342-ecuex64m
author: Fong, Simon James
date: 2020-03-22
url: https://arxiv.org/pdf/2003.09868v1.pdf
doi: nan
title:
  Composite Monte Carlo Decision Making under High Uncertainty of Novel Coronavirus Epidemic Using Hybridized Deep Learning and Fuzzy Rule Induction

abstract:
  In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal-spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy rule induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min-max rules that foretell about the extreme ranges of future possibilities with respect to the epidemic.


id: cord-252984-79jzkdu2
author: Bickman, Leonard
date: 2020-07-26
txt: ./txt/cord-252984-79jzkdu2.txt
cache: ./cache/cord-252984-79jzkdu2.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1007/s10488-020-01065-8
doi: 10.1007/s10488-020-01065-8
summary:
  I describe five principal causes of this failure, which I attribute primarily, but not solely, to methodological limitations of RCTs. Lastly, I make the case for why I think AI and the parallel movement of precision medicine embody approaches that are needed to augment, but probably not replace, our current research and development efforts in the field of mental health services. (1) harmonize terminology and specify MBC''s core components; (2) develop criterion standard methods for monitoring fidelity and reporting quality of implementation; (3) develop algorithms for MBC to guide psychotherapy; (4) test putative mechanisms of change, particularly for psychotherapy; (5) develop brief and psychometrically strong measures for use in combination; (6) assess the critical timing of administration needed to optimize patient outcomes; (7) streamline measurement feedback systems to include only key ingredients and enhance electronic health record interoperability; (8) identify discrete strategies to support implementation; (9) make evidence-based policy decisions; and (10) align reimbursement structures.

title:
  Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health

abstract:
  This conceptual paper describes the current state of mental health services, identifies critical problems, and suggests how to solve them. I focus on the potential contributions of artificial intelligence and precision mental health to improving mental health services. Toward that end, I draw upon my own research, which has changed over the last half century, to highlight the need to transform the way we conduct mental health services research. I identify exemplars from the emerging literature on artificial intelligence and precision approaches to treatment in which there is an attempt to personalize or fit the treatment to the client in order to produce more effective interventions.


id: cord-253918-8g3erth8
title: On the responsible use of digital data to tackle the COVID-19 pandemic
author: Ienca, Marcello
date: 2020-03-27
url: https://doi.org/10.1038/s41591-020-0832-5
doi: 10.1038/s41591-020-0832-5
abstract:
  Large-scale collection of data could help curb the COVID-19 pandemic, but it should not neglect privacy and public trust. Best practices should be identified to maintain responsible data-collection and data-processing standards at a global scale.


id: cord-259247-7loab74f
author: CAPPS, BENJAMIN
date: 2020-06-05
url: https://www.ncbi.nlm.nih.gov/pubmed/32498725/
doi: 10.1017/s0963180120000456
title:
  Where Does Open Science Lead Us During a Pandemic? A Public Good Argument to Prioritize Rights in the Open Commons

abstract:
  During the 2020 COVID-19 pandemic, open science has become central to experimental, public health, and clinical responses across the globe. Open science (OS) is described as an open commons, in which a right to science renders all possible scientific data for everyone to access and use. In this common space, capitalist platforms now provide many essential services and are taking the lead in public health activities. These neoliberal businesses, however, have a problematic role in the capture of public goods. This paper argues that the open commons is a community of rights, consisting of people and institutions whose interests mutually support the public good. If OS is a cornerstone of public health, then reaffirming the public good is its overriding purpose, and unethical platforms ought to be excluded from the commons and its benefits.


id: cord-259929-02765q5j
title: Decoding DNA data storage for investment
author: Stanley, Philip M.
date: 2020-09-28
url: https://www.sciencedirect.com/science/article/pii/S0734975020301415?v=s5
doi: 10.1016/j.biotechadv.2020.107639
abstract:
  While DNA's perpetual role in biology and life science is well documented, its burgeoning digital applications are beginning to garner significant interest. As the development of novel technologies requires continuous research, product development, startup creation, and financing, this work provides an overview of each respective area and highlights current trends, challenges, and opportunities. These are supported by numerous interviews with key opinion leaders from across academia, government agencies and the commercial sector, as well as investment data analysis. Our findings illustrate the societal and economic need for technological innovation and disruption in data storage, paving the way for nature's own time-tested, advantageous, and unrivaled solution. We anticipate a significant increase in available investment capital and continuous scientific progress, creating a ripe environment on which DNA data storage-enabling startups can capitalize to bring DNA data storage into daily life.


id: cord-261809-ccc8wzne
title: Mass Surveillance in the Age of COVID-19
author: Ram, Natalie
date: 2020-05-08
url: https://www.ncbi.nlm.nih.gov/pubmed/32728466/
doi: 10.1093/jlb/lsaa023
abstract:
  Epidemiological surveillance programs such as digital contact tracing have been touted as a silver bullet that will free the American public from the strictures of social distancing, enabling a return to school, work, and socializing. This Article assesses whether and under what circumstances the United States ought to embrace such programs. Part I analyzes the constitutionality of programs like digital contact tracing, arguing that the Fourth Amendment's protection against unreasonable searches and seizures may well regulate the use of location data for epidemiological purposes, but that the legislative and executive branches have significant latitude to develop these programs within the broad constraints of the ``special needs'' doctrine elaborated by the courts in parallel circumstances. Part II cautions that the absence of a firm warrant requirement for digital contact tracing should not serve as a green light for unregulated and mass digital location tracking. In light of substantial risks to privacy, policy makers must ask hard questions about efficacy and the comparative advantages of location tracking versus more traditional means of controlling epidemic contagions, take seriously threats to privacy, tailor programs parsimoniously, establish clear metrics for determining success, and set clear plans for decommissioning surveillance programs.


id: cord-264994-j8iawzp8
title: Modelling microbial infection to address global health challenges
author: Fitzpatrick, Meagan C.
date: 2019-09-20
txt: ./txt/cord-264994-j8iawzp8.txt
cache: ./cache/cord-264994-j8iawzp8.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1038/s41564-019-0565-8
doi: 10.1038/s41564-019-0565-8
summary:
  Epidemiological modelling is a tool that can be used to mitigate this risk by predicting disease spread or quantifying the impact of different intervention strategies on disease transmission dynamics. Epidemiological modelling is a tool that can be used to mitigate this risk by predicting disease spread or quantifying the impact of different intervention strategies on disease transmission dynamics. We illustrate how four decades of methodological advances and improved data quality have facilitated the contribution of modelling to address global health challenges, exemplified by models for the HIV crisis, emerging pathogens and pandemic preparedness. We illustrate how four decades of methodological advances and improved data quality have facilitated the contribution of modelling to address global health challenges, exemplified by models for the HIV crisis, emerging pathogens and pandemic preparedness. Compartmental models analysing the interplay between vaccine uptake and disease dynamics confirmed the hypothesis that increases in vaccination were a response to the pertussis infection risk 61 , and showed that incorporating this interplay can improve epidemiological forecasts.

abstract:
  The continued growth of the world’s population and increased interconnectivity heighten the risk that infectious diseases pose for human health worldwide. Epidemiological modelling is a tool that can be used to mitigate this risk by predicting disease spread or quantifying the impact of different intervention strategies on disease transmission dynamics. We illustrate how four decades of methodological advances and improved data quality have facilitated the contribution of modelling to address global health challenges, exemplified by models for the HIV crisis, emerging pathogens and pandemic preparedness. Throughout, we discuss the importance of designing a model that is appropriate to the research question and the available data. We highlight pitfalls that can arise in model development, validation and interpretation. Close collaboration between empiricists and modellers continues to improve the accuracy of predictions and the optimization of models for public health decision-making.


id: cord-266626-9vn6yt8m
title: Agile Clinical Research: A Data Science Approach to Scrumban in Clinical Medicine
author: Lei, Howard
date: 2020-10-22
url: https://api.elsevier.com/content/article/pii/S2666521220300090
doi: 10.1016/j.ibmed.2020.100009
abstract:
  The COVID-19 pandemic has required greater minute-to-minute urgency of patient treatment in Intensive Care Units (ICUs), rendering the use of Randomized Controlled Trials (RCTs) too slow to be effective for treatment discovery. There is a need for agility in clinical research, and the use of data science to develop predictive models for patient treatment is a potential solution. However, rapidly developing predictive models in healthcare is challenging given the complexity of healthcare problems and the lack of regular interaction between data scientists and physicians. Data scientists can spend significant time working in isolation to build predictive models that may not be useful in clinical environments. We propose the use of an agile data science framework based on the Scrumban framework used in software development. Scrumban is an iterative framework, where in each iteration larger problems are broken down into simple do-able tasks for data scientists and physicians. The two sides collaborate closely in formulating clinical questions and developing and deploying predictive models into clinical settings. Physicians can provide feedback or new hypotheses given the performance of the model, and refinement of the model or clinical questions can take place in the next iteration. The rapid development of predictive models can now be achieved with increasing numbers of publicly available healthcare datasets and easily accessible cloud-based data science tools. What is truly needed are data scientist and physician partnerships ensuring close collaboration between the two sides in using these tools to develop clinically useful predictive models to meet the demands of the COVID-19 healthcare landscape.


id: cord-266898-f00628z4
author: Nikitenkova, S.
date: 2020-06-03
txt: ./txt/cord-266898-f00628z4.txt
cache: ./cache/cord-266898-f00628z4.txt
extension: .txt
mime: text/plain
url: http://medrxiv.org/cgi/content/short/2020.06.01.20118869v1?rss=1
doi: 10.1101/2020.06.01.20118869
summary:
  Processing statistical data of countries that have reached an epidemic peak has shown that this regular monitoring obeys a simple analytical regularity which allows us to answer the question: is this or that country that has already passed the threshold of the epidemic close to its peak or is still far from it? To achieve this goal, it is necessary to identify, evaluate and study the mentioned regular component of the error, using the statistics of those countries that have already reached a peak -the stationary level of the epidemic dynamics. This regular error component explains the reason for the failure of a priori mathematical modelling of probable epidemic events in different countries of the world. Processing statistical data of countries that have reached an epidemic peak has shown that this regular monitoring obeys a simple analytical regularity.

title:
  It''s the very time to learn a pandemic lesson: why have predictive techniques been ineffective when describing long-term events?

abstract:
  We have detected a regular component of the monitoring error of officially registered total cases of the spread of the current pandemic. This regular error component explains the reason for the failure of a priori mathematical modelling of probable epidemic events in different countries of the world. Processing statistical data of countries that have reached an epidemic peak has shown that this regular monitoring obeys a simple analytical regularity which allows us to answer the question: is this or that country that has already passed the threshold of the epidemic close to its peak or is still far from it?


id: cord-267485-1fu1blu0
title: Distributed data processing for public health surveillance
author: Lazarus, Ross
date: 2006-09-19
txt: ./txt/cord-267485-1fu1blu0.txt
cache: ./cache/cord-267485-1fu1blu0.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/16984658/
doi: 10.1186/1471-2458-6-235
summary:
  All PHI in this system is initially processed within the secured infrastructure of the health care provider that collects and holds the data, using uniform software distributed and supported by the NDP. In the more traditional type of system, individual patient records, often containing potentially identifiable information, such as date of birth and exact or approximate home address, are transferred, usually in electronic form, preferably through some secured method, to a central secured repository, where statistical tools can be used to develop and refine surveillance procedures. These standard line lists are used most often to support requests by public health agencies for additional information about the individual cases that contribute to clusters identified in the aggregate data. In our experience, such requests involve only a tiny fraction of the data that would be transferred in a centralized surveillance model, providing adequate support for public health with minimal risk of inadvertent disclosure of identifiable PHI.

abstract:
  BACKGROUND: Many systems for routine public health surveillance rely on centralized collection of potentially identifiable, individual, identifiable personal health information (PHI) records. Although individual, identifiable patient records are essential for conditions for which there is mandated reporting, such as tuberculosis or sexually transmitted diseases, they are not routinely required for effective syndromic surveillance. Public concern about the routine collection of large quantities of PHI to support non-traditional public health functions may make alternative surveillance methods that do not rely on centralized identifiable PHI databases increasingly desirable. METHODS: The National Bioterrorism Syndromic Surveillance Demonstration Program (NDP) is an example of one alternative model. All PHI in this system is initially processed within the secured infrastructure of the health care provider that collects and holds the data, using uniform software distributed and supported by the NDP. Only highly aggregated count data is transferred to the datacenter for statistical processing and display. RESULTS: Detailed, patient level information is readily available to the health care provider to elucidate signals observed in the aggregated data, or for ad hoc queries. We briefly describe the benefits and disadvantages associated with this distributed processing model for routine automated syndromic surveillance. CONCLUSION: For well-defined surveillance requirements, the model can be successfully deployed with very low risk of inadvertent disclosure of PHI – a feature that may make participation in surveillance systems more feasible for organizations and more appealing to the individuals whose PHI they hold. It is possible to design and implement distributed systems to support non-routine public health needs if required.


id: cord-269693-9tsy79lt
title: Multistage implementation framework for smart supply chain management under industry 4.0
author: Shao, Xue-Feng
date: 2020-10-06
txt: ./txt/cord-269693-9tsy79lt.txt
cache: ./cache/cord-269693-9tsy79lt.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S004016252031180X
doi: 10.1016/j.techfore.2020.120354
summary:
  Industry 4.0, or smart manufacturing, are the terms that are being used for digital transformation, using technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Cloud Computing (CC), Machine Learning (ML), and Data Analytics (DA), etc. Many researchers have explained the phenomena of smart manufacturing, or industry 4.0 technologies, in terms of an augmented and virtual reality (Wu et al., 2013; Rüßmann et al., 2015; Kolberg and Zühlke, 2015) , additive manufacturing (Huang et al., 2013; Chan et al., 2018) , internet of things (Wu et al., 2017) , big data analytics (De Mauro et al., 2015; Addo-Tenkorang and Helo, 2016; Lenz et al.,2018) , and cyber-physical systems (Monostori, 2014; Lee et al., 2015; Zhong and Nof, 2015) . The organization for this study was selected using the theoretical sampling, as it provided the opportunity to capture the evolution of the industry 4.0 implementation across a supply chain that included the focal firm, along with its supplier and a downstream customer (Eisenhardt, 1989 , Siggelkow, 2007 .

abstract:
  The true potential of the industry 4.0, which is a byproduct of the fourth industrial revolution, cannot be actually realized. This is, of course true, until the smart factories in the supply chains get connected to each other, with their systems and the machines linked to a common networking system. The last few years have experienced an increase in the adoption and acceptance of the industry 4.0′s components. However, the next stage of smart factories, which will be the smart supply chains, is still in its period of infancy. Moreover, there is a simultaneous need to maintain a focus on the supply chain level implementation of the concept that industry 4.0 puts forth. This is important in order to gain the end to end benefits, while also avoiding the organization to organization compatibility issues that may follow later on. When considering this concept, limited research exists on the issues related to the implementation of industry 4.0, at the supply chain level. Hence, keeping in mind this lack of literature and research available, on a phenomenon that will define the future of business and industry, this study uses an exploratory approach to capture the implementation of industry 4.0 concepts across multiple tiers of the supply chain. Based on this research, the study proposes a multistage implementation framework that highlights the organizational enablers such as culture, cross-functional approach, and the continuous improvement activities. Furthermore, it also highlights the staged implementation of the advanced tools, starting from the focal organization with the subsequent integration with the partner organizations.


id: cord-270703-c8mv2eve
author: Christensen, Paul A
date: 2018-11-30
txt: ./txt/cord-270703-c8mv2eve.txt
cache: ./cache/cord-270703-c8mv2eve.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/30619910/
doi: 10.1093/ofid/ofy322
summary:
  We implemented a real-time report to distribute respiratory pathogen data for our 8-hospital system to anyone with an Internet connection and a web browser. We implemented a real-time report to distribute respiratory pathogen data for our 8-hospital system to anyone with an Internet connection and a web browser. To address these local needs in a major US metropolitan area, our clinical microbiology laboratory implemented an online dashboard to distribute respiratory pathogen data for our 8-hospital system to clinicians, epidemiologists, infection control practitioners, system leadership, and the public. Development of this report began in the Fall 2017, before the respiratory virus season, during which influenza reached an epidemic status across the United States that resulted in supply shortages, testing difficulties, and a widespread public health crisis [4, 5] . In summary, our microbiology laboratory implemented a near real-time Internet report to distribute respiratory pathogen data for our 8-hospital system to clinicians, hospital epidemiologists, infection control committees, system leadership, and the public.

title:
  Real-time Communication With Health Care Providers Through an Online Respiratory Pathogen Laboratory Report

abstract:
  We implemented a real-time report to distribute respiratory pathogen data for our 8-hospital system to anyone with an Internet connection and a web browser. Real-time access to accurate regional laboratory observation data during an epidemic influenza season can guide diagnostic and therapeutic strategies.


id: cord-270721-81axdn0g
author: Allam, Zaheer
date: 2020-07-24
txt: ./txt/cord-270721-81axdn0g.txt
cache: ./cache/cord-270721-81axdn0g.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/B978012824313800005X
doi: 10.1016/b978-0-12-824313-8.00005-x
summary:
  In view of required immediate actions, volunteered geographic information (VGI) and citizen science concept have emerged, where people voluntarily share location and health status data to circumvent data sharing restrictions imposed upon corporations and governments. With all these, in the case of COVID-19, startups engaged in providing more insights are observed to access data from those sources, including airline ticketing and from governments of different countries, and with these, they are able to run simulation and predictive algorithms to come up with conclusions guiding policy orientations. Such were shared by BlueDot and Metabiota, some of the modern startups that use data, and through advanced technologies, such as natural language processing and machine learning, they were able to predict some of the geographical location that the virus would spread next from Wuhan, days before first cases were reported in those regions. On the Coronavirus (COVID-19) Outbreak and the Smart City Network: Universal Data Sharing Standards Coupled with Artificial Intelligence (AI) to Benefit Urban Health Monitoring and Management

title:
  The Emergence of Voluntary Citizen Networks to Circumvent Urban Health Data Sharing Restrictions During Pandemics

abstract:
  COVID-19 has impacted the global landscape well beyond initial estimates, impacting on both societal and economic fronts. Immediate responses by corporations and governments were geared toward building knowledge so that accurate and efficient programs could be devised toward curbing the impacts of the pandemic on society. However, one aspect to this was noted as to the limited availability of data sharing across platforms, systems, and jurisdictions, leading to limited datasets, hence, rendering inaccurate predictions that can be used to contain and limit the virus outbreak. In view of required immediate actions, volunteered geographic information (VGI) and citizen science concept have emerged, where people voluntarily share location and health status data to circumvent data sharing restrictions imposed upon corporations and governments. This is leading to more accurate predictions and supporting an emergence of alternative tools. This chapter explores this dimension and outlines how people, previously aggressively resisting data sharing, do so willingly in times of emergencies.


id: cord-272276-83f0ruku
author: Wagner, Joseph E.
date: 1984-12-31
txt: ./txt/cord-272276-83f0ruku.txt
cache: ./cache/cord-272276-83f0ruku.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/6380915/
doi: 10.1016/0010-4825(84)90033-7
summary:
  Abstract Substantial data collected from large numbers of accessions, the need for comprehensive reporting of negative as well as positive laboratory findings, and the necessity for obtaining rapid diagnostic correlations prompted the development of a computer based system of accession data management for collection, storage, rapid retrieval, reporting, concording, and administrative compiling in a state-university Veterinary Medical Diagnostic Laboratory. Demographic-zoographic panel ( Fig. 1) When an accession is presented to the RADIL section of the Veterinary Medical 12 13 14 15 16 17 18 19 20 21 22 23 24 ,ll,l111lll~llLllll1111111111111111~1111~~~~~~~~~~~~''~~~~~~"''~~"''~''~''~~~~~~~~~ Diagnostic Laboratory, demographic and zoographic information is immediately entered by a data controller or data entry operator from information on a form submitted with the accession. Reports of negative findings and normal necropsy observations, as well as reports of the kinds of techniques used (such as the kind of blood collection method used, arrow, Fig. 2 , line 8) can be entered by a code number, thus reducing data entry time.

title:
  A computer based system for collection, storage, retrieval and reporting accession information in a veterinary medical diagnostic laboratory

abstract:
  Abstract Substantial data collected from large numbers of accessions, the need for comprehensive reporting of negative as well as positive laboratory findings, and the necessity for obtaining rapid diagnostic correlations prompted the development of a computer based system of accession data management for collection, storage, rapid retrieval, reporting, concording, and administrative compiling in a state-university Veterinary Medical Diagnostic Laboratory.


id: cord-273163-xm6qvhn1
title: Fighting pandemics with digital epidemiology
author: Tarkoma, Sasu
date: 2020-08-25
txt: ./txt/cord-273163-xm6qvhn1.txt
cache: ./cache/cord-273163-xm6qvhn1.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S258953702030256X
doi: 10.1016/j.eclinm.2020.100512
abstract: nan
summary:
  Digital epidemiologists conduct traditional epidemiological studies and health-related research using new data sources and digital methods from data collection to analysis [1, 2] . Digital epidemiology and digital tools have had a profound role in understanding and mitigating the COVID-19 pandemic through analysis of diverse digital data sources such as smartphone, health register, and environmental monitoring data. Combining aggregate and privacy-protected diverse data sources such as mobility, health, environmental, and city data is expected to help understand and mitigate the consequences of pandemics. The digital epidemiology toolkit is likely to be supported by advances in ML, privacy-enhancing technologies, data/ model validation and explainability, and national and transnational policy measures. Increasing data availability and access combined with advances in open source data processing and analysis pave the way for scalable digital epidemiology supporting world health security.


id: cord-274019-dao10kx9
title: Phylodynamic applications in 21(st) century global infectious disease research
author: Rife, Brittany D
date: 2017-05-08
txt: ./txt/cord-274019-dao10kx9.txt
cache: ./cache/cord-274019-dao10kx9.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1186/s41256-017-0034-y
doi: 10.1186/s41256-017-0034-y
summary:
  These innovative tools have greatly enhanced scientific investigations of the temporal and geographical origins, evolutionary history, and ecological risk factors associated with the growth and spread of viruses such as human immunodeficiency virus (HIV), Zika, and dengue and bacteria such as Methicillin-resistant Staphylococcus aureus. CONCLUSIONS: Capitalizing on an extensive review of the literature, we discuss the evolution of the field of infectious disease epidemiology and recent accomplishments, highlighting the advancements in phylodynamics, as well as the challenges and limitations currently facing researchers studying emerging pathogen epidemics across the globe. The reliance on phylodynamic methods for estimating a pathogen''s population-level characteristics (e.g., effective population size) and their relationships with epidemiological data suffers from a high costincreasing the number of inference models, and thus parameters associated with these models, requires an even greater increase in the information content, or phylogenetic resolution, of the sequence alignment and associated phenotypic data.

abstract:
  BACKGROUND: Phylodynamics, the study of the interaction between epidemiological and pathogen evolutionary processes within and among populations, was originally defined in the context of rapidly evolving viruses and used to characterize transmission dynamics. The concept of phylodynamics has evolved since the early 21(st) century, extending its reach to slower-evolving pathogens, including bacteria and fungi, and to the identification of influential factors in disease spread and pathogen population dynamics. RESULTS: The phylodynamic approach has now become a fundamental building block for the development of comparative phylogenetic tools capable of incorporating epidemiological surveillance data with molecular sequences into a single statistical framework. These innovative tools have greatly enhanced scientific investigations of the temporal and geographical origins, evolutionary history, and ecological risk factors associated with the growth and spread of viruses such as human immunodeficiency virus (HIV), Zika, and dengue and bacteria such as Methicillin-resistant Staphylococcus aureus. CONCLUSIONS: Capitalizing on an extensive review of the literature, we discuss the evolution of the field of infectious disease epidemiology and recent accomplishments, highlighting the advancements in phylodynamics, as well as the challenges and limitations currently facing researchers studying emerging pathogen epidemics across the globe. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s41256-017-0034-y) contains supplementary material, which is available to authorized users.


id: cord-275069-opuwyaiv
author: Amram, Denise
date: 2020-07-31
txt: ./txt/cord-275069-opuwyaiv.txt
cache: ./cache/cord-275069-opuwyaiv.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0267364920300182
doi: 10.1016/j.clsr.2020.105413
summary:
  For this reason, considering the new ethical-legal issues emerging from the scientific-technological progress that involves a daily use of health-related data, our comparative analysis will firstly discuss the legal bases for health data processing for research purposes in order to identify the critical profiles as well possible practical solutions that might help Ulysses 4.0. Some critical profiles emerge from article 9, para 4, GDPR which allows Member States to decide whether or not maintaining the legal bases provided by the EU Regulation or introducing further conditions, including limitations, with regard to the processing of particularly sensitive data, like the genetic data, the biometric ones, or those concerning health. According to the above-discussed system, the data controller (i.e. the university/research institute in person of the legal representative) shall involve the principal investigator in the data management activities, authorizing to data processing under article 29 GDPR, in order to proactively guarantee the adoption of those technical and organizational measures aimed at safeguarding the rights and freedoms of data subjects in her project.

title:
  Building up the “Accountable Ulysses” model. The impact of GDPR and national implementations, ethics, and health-data research: Comparative remarks

abstract:
  Abstract The paper illustrates obligations emerging under articles 9 and 89 of the EU Reg. 2016/679 (General Data Protection Regulation, hereinafter “GDPR”) within the health-related data processing for research purposes. Furthermore, through a comparative analysis of the national implementations of the GDPR on the topic, the paper highlights few practical issues that the researcher might deal with while accomplishing the GDPR obligations and the other ethical requirements. The result of the analyses allows to build up a model to achieve an acceptable standard of accountability in health-related data research. The legal remarks are framed within the myth of Ulysses.


id: cord-275300-4phjvxat
author: Galván‐Casas, C.
date: 2020-06-22
txt: ./txt/cord-275300-4phjvxat.txt
cache: ./cache/cord-275300-4phjvxat.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32569387/
doi: 10.1111/bjd.19317
summary:
  key: cord-275300-4phjvxat title: Sars‐CoV‐2 infection: the same virus can cause different cutaneous manifestations: reply from authors cord_uid: 4phjvxat We have reported and included in the supplementary material a few cases that were noticed by their doctors and were the first descriptions of enanthem in COVID‐19. Given the low number of cases and their non‐systematic acquisition, we avoided any analysis of these data. We have reported and included in the supplementary material a few cases that were noticed by their doctors and were the first descriptions of enanthem in COVID-19. Given the low number of cases and their non-systematic acquisition, we avoided any analysis of these data. All the included patients gave informed consent before incorporating their data in the study. Sars-CoV-2 infection: the same virus can cause different cutaneous manifestations Classification of the cutaneous manifestations of COVID-19: a rapid prospective nationwide consensus study in Spain with 375 cases

title:
  Sars‐CoV‐2 infection: the same virus can cause different cutaneous manifestations: reply from authors

abstract:
  Dr Drago et al. are right to point out that our paper did not provide data on enanthems(1,2). As the data collection form did not include the description of mucous membranes, they might have not been explored in many patients. We have reported and included in the supplementary material a few cases that were noticed by their doctors and were the first descriptions of enanthem in COVID‐19. Given the low number of cases and their non‐systematic acquisition, we avoided any analysis of these data.


id: cord-275742-7jxt6diq
title: Preventive healthcare policies in the US: solutions for disease management using Big Data Analytics
author: Batarseh, Feras A.
date: 2020-06-23
txt: ./txt/cord-275742-7jxt6diq.txt
cache: ./cache/cord-275742-7jxt6diq.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32834926/
doi: 10.1186/s40537-020-00315-8
summary:
  Our work''s main objective (hypothesis) is two-tier: through one of the largest and most representative national health datasets for population-based surveillance, data imputations and machine learning models (such as clustering) offer preventive care pointers by grouping patients into heterogeneous clusters, and providing data-driven predictions and policies for healthcare in the US. The Center for Disease Control and Prevention (CDC) reported on those states, and presented multiple cases to help increase public trust in immunizations: "We hope this report is a reminder to healthcare professionals to make a strong vaccine recommendation to their patients at every visit and make sure parents understand how important it is for their children to get all their recommended vaccinations on time" [5, 8] . 2. We aim to collect more CDC data variables to provide more correlations and further tests for imputations, and compare with other NHANES predictive models for specific diseases such as periodontitis [39] .

abstract:
  Data-driven healthcare policy discussions are gaining traction after the Covid-19 outbreak and ahead of the 2020 US presidential elections. The US has a hybrid healthcare structure; it is a system that does not provide universal coverage, albeit few years ago enacted a mandate (Affordable Care Act-ACA) that provides coverage for the majority of Americans. The US has the highest health expenditure per capita of all western and developed countries; however, most Americans don’t tap into the benefits of preventive healthcare. It is estimated that only 8% of Americans undergo routine preventive screenings. On a national level, very few states (15 out of the 50) have above-average preventive healthcare metrics. In literature, many studies focus on the cure of diseases (research areas such as drug discovery and disease prediction); whilst a minority have examined data-driven preventive measures—a matter that Americans and policy makers ought to place at the forefront of national issues. In this work, we present solutions for preventive practices and policies through Machine Learning (ML) methods. ML is morally neutral, it depends on the data that train the models; in this work, we make the case that Big Data is an imperative paradigm for healthcare. We examine disparities in clinical data for US patients by developing correlation and imputation methods for data completeness. Non-conventional patterns are identified. The data lifecycle followed is methodical and deliberate; 1000+ clinical, demographical, and laboratory variables are collected from the Centers for Disease Control and Prevention (CDC). Multiple statistical models are deployed (Pearson correlations, Cramer’s V, MICE, and ANOVA). Other unsupervised ML models are also examined (K-modes and K-prototypes for clustering). Through the results presented in the paper, pointers to preventive chronic disease tests are presented, and the models are tested and evaluated.


id: cord-276405-yfvu83r9
author: Brat, Gabriel A.
date: 2020-08-19
txt: ./txt/cord-276405-yfvu83r9.txt
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url: https://doi.org/10.1038/s41746-020-00308-0
doi: 10.1038/s41746-020-00308-0
summary:
  Because EHRs are not themselves agile analytic platforms, we have been successfully building upon the open source and free i2b2 (for Informatics for Integrating Biology and the Bedside) toolkit [10] [11] [12] [13] [14] [15] [16] [17] to manage, compute, and share data extracted from EHRs. In response to COVID-19, we have organized a global community of researchers, most of whom are or have been members of the i2b2 Academic Users Group, to rapidly set up an ad hoc network that can begin to answer some of the clinical and epidemiological questions around COVID-19 through data harmonization, analytics, and visualizations. Laboratory value trajectories Our initial data extraction included 14 laboratory markers of cardiac, renal, hepatic, and immune dysfunction that have been strongly associated with poor outcomes in COVID-19 patients in previous publications.

title:
  International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium

abstract:
  We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.


id: cord-278913-u6vihq3u
title: The Rise of Machine Intelligence in the COVID-19 Pandemic and Its Impact on Health Policy
author: Allam, Zaheer
date: 2020-07-24
txt: ./txt/cord-278913-u6vihq3u.txt
cache: ./cache/cord-278913-u6vihq3u.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/B9780128243138000061
doi: 10.1016/b978-0-12-824313-8.00006-1
summary:
  For instance, despite the challenges raised earlier, some startup companies were able to use the available data from social media, airline ticketing, and medical institutions to identify that the world is experiencing a new virus outbreak days before those in medical fraternity had made similar findings (Gaille, 2019) . According to Niiler (2020) , BlueDot, whose profile is shared in the following, was able to employ the services of AIdriven algorithms, to analyze data gathered from sources such as new reports, air ticketing, and animal disease outbreaks to predict that the world is facing a new type of virus outbreak. In the recent case of COVID-19, Metabiota was in the forefront to analyze the outbreak, and during the analysis of the data, some even sourced from social media, the company was able to predict which neighboring countries were at high risk of being the next target of the virus spread, more so because the panic in Wuhan had stated to trigger some fear, forcing people to flee.

abstract:
  The use of advanced technologies, especially predictive computing in the health sector, is on the rise in this era, and they have successfully transformed the sector with quality insights, better decision-making, and quality policies. Even though notable benefits have been achieved through the uptake of the technologies, adoption is still slow, as most of them are still new, hence facing some hurdles in their applications especially in national and international policy levels. But the recent case of COVID-19 outbreak has given an opportunity to showcase that these technologies, especially artificial intelligence (AI), have the capacity to produce accurate, real-time, and reliable predictions on issues as serious as pandemic outbreak. A case in point is how companies such as BlueDot and Metabiota managed to correctly predict the spread route of the virus days before such events happened and officially announced by the World Health Organization. In this chapter, an increase in the use of AI-based technologies to detect infectious diseases is underlined and how such uses have led to early detections of infectious diseases. Nevertheless, there is evidence that there is need to enhance data sharing activities, especially by rethinking how to improve the efficiency of data protocols. The chapter further proposes the need for enhanced use of technologies and data sharing to ensure that future outbreaks are detected even earlier, thus accelerating early preventive measures.


id: cord-279125-w6sh7xpn
title: Digital microbiology
author: Egli, Adrian
date: 2020-06-27
txt: ./txt/cord-279125-w6sh7xpn.txt
cache: ./cache/cord-279125-w6sh7xpn.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S1198743X20303670
doi: 10.1016/j.cmi.2020.06.023
summary:
  Making an efficient use of big data, machine learning, and artificial intelligence in clinical microbiology requires a profound understanding of data handling aspects. clinical decision support systems based on machine learning to provide automated feedback 7 regarding empiric antibiotic prescription adapted to specific patient groups 46 . As physiology and laboratory parameters can rapidly change 9 during an infection, time-series data greatly impact the predictive values of such algorithms -similar 10 to a doctor, who observers the patient during disease progression -machine learning algorithms will 11 also follow the patient''s data stream. Machine 18 learning algorithms may be used at each step of the microbiological diagnostic process from pre-to 19 post-analytics, helping us to deal with the increasing quantities and complexity of data 113,114 (Table 1) . Machine learning radically changes the way we 8 handle healthcare-related data -including data of clinical microbiology and infectious diseases.

abstract:
  BACKGROUND: Digitalisation and artificial intelligence have an important impact on the way microbiology laboratories will work in the near future. Opportunities and challenges lay ahead to digitalise the microbiological workflows. Making an efficient use of big data, machine learning, and artificial intelligence in clinical microbiology requires a profound understanding of data handling aspects. OBJECTIVE: This review article summarizes the most important concepts of digital microbiology. The article provides microbiologists, clinicians and data scientists a viewpoint and practical examples along the diagnostic process. SOURCES: We used peer-reviewed literature identified by a Pubmed search for digitalisation, machine learning, artificial intelligence and microbiology. CONTENT: We describe the opportunities and challenges of digitalisation in microbiological diagnostic process with various examples. We also provide in this context key aspects of data structure and interoperability, as well as legal aspects. Finally, we outline the way for applications in a modern microbiology laboratory. IMPLICATIONS: We predict that digitalization and the usage of machine learning will have a profound impact on the daily routine of the laboratory staff. Along the analytical process, the most important steps should be identified, where digital technologies can be applied and provide a benefit. The education of all staff involved should be adapted to prepare for the advances in digital microbiology.


id: cord-282724-zzkqb0u2
title: Ideas for how informaticians can get involved with COVID-19 research
author: Moore, Jason H.
date: 2020-05-12
txt: ./txt/cord-282724-zzkqb0u2.txt
cache: ./cache/cord-282724-zzkqb0u2.txt
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mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32419848/
doi: 10.1186/s13040-020-00213-y
summary:
  Some key considerations and targets of research include: (1) feature engineering, transforming raw data into features (i.e. variables) that ML can better utilize to represent the problem/target outcome, (2) feature selection, applying expert domain knowledge, statistical methods, and/or ML methods to remove ''irrelevant'' features from consideration and improve downstream modeling, (3) data harmonization, allowing for the integration of data collected at different sites/institutions, (4) handling different outcomes and related challenges, e.g. binary classification, multi-class, quantitative phenotypes, class imbalance, temporal data, multi-labeled data, censored data, and the use of appropriate evaluation metrics, (5) ML algorithm selection for a given problem can be a challenge in itself, thus strategies to integrate the predictions of multiple machine learners as an ensemble are likely to be important, (6) ML modeling pipeline assembly, including critical considerations such as hyper-parameter optimization, accounting for overfitting, and clinical interpretability of trained models, and (7) considering and accounting for covariates as well as sources of bias in data collection, study design, and application of ML tools in order to avoid drawing conclusions based on spurious correlations.

abstract:
  The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact on population health and wellbeing. Biomedical informatics is central to COVID-19 research efforts and for the delivery of healthcare for COVID-19 patients. Critical to this effort is the participation of informaticians who typically work on other basic science or clinical problems. The goal of this editorial is to highlight some examples of COVID-19 research areas that could benefit from informatics expertise. Each research idea summarizes the COVID-19 application area, followed by an informatics methodology, approach, or technology that could make a contribution. It is our hope that this piece will motivate and make it easy for some informaticians to adopt COVID-19 research projects.


id: cord-282938-1if7bl2u
title: Using Mobile Phone Data for Emergency Management: a Systematic Literature Review
author: Wang, Yanxin
date: 2020-09-16
txt: ./txt/cord-282938-1if7bl2u.txt
cache: ./cache/cord-282938-1if7bl2u.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32952439/
doi: 10.1007/s10796-020-10057-w
summary:
  Three research objectives are undertaken to achieve the goal of synthesizing the fragmented knowledge and providing research guidance: (i) extract basic knowledge (e.g. types of mobile phone data, situations) of EM from the selected studies; (ii) break the boundaries of different disciplines and aggregate each analysis perspective; and (iii) study the identified knowledge and integrate it into a single framework that draws a comprehensive map of existing findings under this subject, and provides future implications. Two iterations were processed: (1) searching 26 terms in the keywords list ("mobile phone data" OR "short message service" OR "call detail record" OR "phone GPS data" OR "cellular network data" OR "app data" OR "application data" OR "Bluetooth data") AND ("emergency" OR "extreme situation" OR "extreme event" OR "large-scale event" OR "special event" OR "special situation" OR "anomalous event" OR "anomalous situation" OR "unusual event" OR "unusual situation" OR "crisis" OR"disaster" OR "catastrophe" OR "traffic accident" OR "epidemics" OR "infectious disease") AND (2013 < PUBYEAR<2019); (2) searching papers in the reference list of the five previously identified review articles and including additional studies.

abstract:
  Emergency management (EM) has always been a concern of people from all walks of life due to the devastating impacts emergencies can have. The global outbreak of COVID-19 in 2020 has pushed EM to the top topic. As mobile phones have become ubiquitous, many scholars have shown interest in using mobile phone data for EM. This paper presents a systematic literature review about the use of mobile phone data for EM that includes 65 related articles written between 2014 and 2019 from six electronic databases. Five themes in using mobile phone data for EM emerged from the reviewed articles, and a systematic framework is proposed to illustrate the current state of the research. This paper also discusses EM under COVID-19 pandemic and five future implications of the proposed framework to guide future work.


id: cord-285379-ljg475sj
title: Digital Health in Electrophysiology and the COVID-19 Global Pandemic
author: Slotwiner, David J.
date: 2020-10-03
txt: ./txt/cord-285379-ljg475sj.txt
cache: ./cache/cord-285379-ljg475sj.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/33043310/
doi: 10.1016/j.hroo.2020.09.003
summary:
  The tools of digital health are facilitating a much needed paradigm shift to a more patient-centric health care delivery system, yet our healthcare infrastructure is firmly rooted in a 20 th Century model which was not designed to receive medical data from outside the traditional medical environment. The tools of digital health are facilitating a much needed paradigm shift to a more patient-centric health care delivery system, yet our healthcare infrastructure is firmly rooted in a 20 th Century model which was not designed to receive medical data from outside the traditional medical environment. In this article, we describe the present state of heart rhythm digital health tools highlighting some of the effects of J o u r n a l P r e -p r o o f the COVID-19 pandemic and propose ways to develop innovative workflows and technological solutions that will make it possible for practices to efficiently process and manage information.

abstract:
  The tools of digital health are facilitating a much needed paradigm shift to a more patient-centric health care delivery system, yet our healthcare infrastructure is firmly rooted in a 20(th) Century model which was not designed to receive medical data from outside the traditional medical environment. COVID-19 has accelerated this adoption and illustrated the challenges that lie ahead as we make this shift. The diverse ecosystem of digital health tools share one feature in common: they generate data which must be processed, triaged, acted upon and incorporated into the longitudinal electronic health record. Critical abnormal findings must be identified and acted upon rapidly, while semi-urgent and non-critical data and trends may be reviewed within a less urgent timeline. Clinically irrelevant findings, which presently comprise a significant percentage of the alerts, ideally would be removed to optimize the high cost, high value resource; i.e., the clinicians’ attention and time. We need to transform our established health care infrastructure, technologies and workflows to be able to safely, effectively and efficiently manage the vast quantities of data that these tools will generate. This must include both new technologies from industry as well as expert consensus documents from medical specialty societies including the Heart Rhythm Society. Ultimately, research will be fundamental to inform effective development and implementation of these tools.


id: cord-285522-3gv6469y
title: Social big data: Recent achievements and new challenges
author: Bello-Orgaz, Gema
date: 2015-08-28
txt: ./txt/cord-285522-3gv6469y.txt
cache: ./cache/cord-285522-3gv6469y.txt
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mime: text/plain
url: https://doi.org/10.1016/j.inffus.2015.08.005
doi: 10.1016/j.inffus.2015.08.005
summary:
  Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. Currently, the exponential growth of social media has created serious problems for traditional data analysis algorithms and techniques (such as data mining, statistics, machine learning, and so on) due to their high computational complexity for large datasets. This section provides a description of the basic methods and algorithms related to network analytics, community detection, text analysis, information diffusion, and information fusion, which are the areas currently used to analyse and process information from social-based sources.

abstract:
  Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media and big data paradigms.


id: cord-286288-gduhterq
title: Cardiovascular Clinical Trials in a Pandemic: Immediate Implications of Coronavirus Disease 2019
author: Spitzer, Ernest
date: 2020-05-01
txt: ./txt/cord-286288-gduhterq.txt
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extension: .txt
mime: text/plain
url: https://doi.org/10.15420/cfr.2020.07
doi: 10.15420/cfr.2020.07
summary:
  Nevertheless, new or ongoing clinical trials, not related to the disease itself, remain important for the development of new therapies, and require interactions among patients, clinicians and research personnel, which is challenging, given isolation measures. Trials in patient populations with acute presentations (e.g. ST-elevation MI [STEMI]) may identify potentially suitable trial candidates; however, the capacity to comply with study procedures needs to be assessed, as well as considerations related to patient safety during follow-up. Participants in the follow-up phase (when they are generally at home) constitute a higher-risk population in the Reduced capacity at investigational sites will impact on availability to perform study visits (or phone calls) to assess and confirm eligibility, enter data in electronic case report forms (eCRFs), to report (serious) adverse events and to follow the protocol in general. The participation of several committees in clinical trials ensures proper scientific and operational oversight, data integrity and quality, as well as patient safety.

abstract:
  The coronavirus disease 2019 (COVID-19) pandemic started in Wuhan, Hubei Province, China, in December 2019, and by 24 April 2020, it had affected >2.73 million people in 185 countries and caused >192,000 deaths. Despite diverse societal measures to reduce transmission of the severe acute respiratory syndrome coronavirus 2, such as implementing social distancing, quarantine, curfews and total lockdowns, its control remains challenging. Healthcare practitioners are at the frontline of defence against the virus, with increasing institutional and governmental supports. Nevertheless, new or ongoing clinical trials, not related to the disease itself, remain important for the development of new therapies, and require interactions among patients, clinicians and research personnel, which is challenging, given isolation measures. In this article, the authors summarise the acute effects and consequences of the COVID-19 pandemic on current cardiovascular trials.


id: cord-287027-ahoo6j3o
title: Unsupervised Learning for County-Level Typological Classification for COVID-19 Research
author: Lai, Yuan
date: 2020-08-30
txt: ./txt/cord-287027-ahoo6j3o.txt
cache: ./cache/cord-287027-ahoo6j3o.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.ibmed.2020.100002
doi: 10.1016/j.ibmed.2020.100002
summary:
  The analysis of county-level COVID-19 pandemic data faces computational and analytic challenges, particularly when considering the heterogeneity of data sources with variation in geographic, demographic, and socioeconomic factors between counties. The purpose of this study is to summarize publicly available and relevant COVID-19 data sources, to address the benchmarking challenge from the data heterogeneity through clustering, and to classify counties J o u r n a l P r e -p r o o f based on their underlying variations. Particularly at the county-level, previous studies have implemented clustering techniques to analyze various data sources relating J o u r n a l P r e -p r o o f to demographic, geographic, environment, and socioeconomic determinants of health and disease. While previous findings reveal possible geographical clusters of COVID-19 cases at the county-level, our study indicates this is from the underlying typology based on high-dimensional variables.

abstract:
  The analysis of county-level COVID-19 pandemic data faces computational and analytic challenges, particularly when considering the heterogeneity of data sources with variation in geographic, demographic, and socioeconomic factors between counties. This study presents a method to join relevant data from different sources to investigate underlying typological effects and disparities across typologies. Both consistencies within and variations between urban and non-urban counties are demonstrated. When different county types were stratified by age group distribution, this method identifies significant community mobility differences occurring before, during, and after the shutdown. Counties with a larger proportion of young adults (age 20-24) have higher baseline mobility and had the least mobility reduction during the lockdown.


id: cord-287884-qxk1wfk8
title: Information technologies of 21st century and their impact on the society
author: Yamin, Mohammad
date: 2019-08-16
txt: ./txt/cord-287884-qxk1wfk8.txt
cache: ./cache/cord-287884-qxk1wfk8.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1007/s41870-019-00355-1
doi: 10.1007/s41870-019-00355-1
summary:
  Some of these technologies are Big Data Analytics, Internet of Things (IoT), Sensor networks (RFID, Location based Services), Artificial Intelligence (AI), Robotics, Blockchain, Mobile digital Platforms (Digital Streets, towns and villages), Clouds (Fog and Dew) computing, Social Networks and Business, Virtual reality. Accordingly, things (technologies, devices and tools) used together in internet based applications to generate data to provide assistance and services to the users from anywhere, at any time. IoT is providing some amazing applications in tandem with wearable devices, sensor networks, Fog computing, and other technologies to improve some the critical facets of our lives like healthcare management, service delivery, and business improvements. Some of the key devices and associated technologies to IoT include RFID Tags [25] , Internet, computers, cameras, RFID, Mobile Devices, coloured lights, RFIDs, Sensors, Sensor networks, Drones, Cloud, Fog and Dew. Blockchain is usually associated with Cryptocurrencies like Bitcoin (Currently, there are over one and a half thousand cryptocurrencies and the numbers are still rising).

abstract:
  Twenty first century has witnessed emergence of some ground breaking information technologies that have revolutionised our way of life. The revolution began late in 20th century with the arrival of internet in 1995, which has given rise to methods, tools and gadgets having astonishing applications in all academic disciplines and business sectors. In this article we shall provide a design of a ‘spider robot’ which may be used for efficient cleaning of deadly viruses. In addition, we shall examine some of the emerging technologies which are causing remarkable breakthroughs and improvements which were inconceivable earlier. In particular we shall look at the technologies and tools associated with the Internet of Things (IoT), Blockchain, Artificial Intelligence, Sensor Networks and Social Media. We shall analyse capabilities and business value of these technologies and tools. As we recognise, most technologies, after completing their commercial journey, are utilised by the business world in physical as well as in the virtual marketing environments. We shall also look at the social impact of some of these technologies and tools.


id: cord-288264-xs08g2cy
title: A citizen science initiative for open data and visualization of COVID-19 outbreak in Kerala, India
author: Ulahannan, Jijo Pulickiyil
date: 2020-08-06
txt: ./txt/cord-288264-xs08g2cy.txt
cache: ./cache/cord-288264-xs08g2cy.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1093/jamia/ocaa203
doi: 10.1093/jamia/ocaa203
summary:
  MATERIALS AND METHODS: Through a citizen science initiative, we leveraged publicly available and crowd-verified data on COVID-19 outbreak in Kerala from the government bulletins and media outlets to generate reusable datasets. RESULTS: From the sourced data, we provided real-time analysis, and daily updates of COVID-19 cases in Kerala, through a user-friendly bilingual dashboard (https://covid19kerala.info/) for non-specialists. CONCLUSION: We reported a citizen science initiative on the COVID-19 outbreak in Kerala to collect and deposit data in a structured format, which was utilized for visualizing the outbreak trend and describing demographic characteristics of affected individuals. Here, we report a citizen science initiative to leverage publicly available data on COVID-19 cases in Kerala from the daily bulletins released by the DHS, Government of Kerala, and various news outlets. The multi-sourced data was refined to make a structured live dataset to provide real-time analysis and daily updates of COVID-19 cases in Kerala through a bilingual (English and Malayalam) user-friendly dashboard (https://covid19kerala.info/).

abstract:
  OBJECTIVE: India reported its first COVID-19 case in the state of Kerala and an outbreak initiated subsequently. The Department of Health Services, Government of Kerala, initially released daily updates through daily textual bulletins for public awareness to control the spread of the disease. However, this unstructured data limits upstream applications, such as visualization, and analysis, thus demanding refinement to generate open and reusable datasets. MATERIALS AND METHODS: Through a citizen science initiative, we leveraged publicly available and crowd-verified data on COVID-19 outbreak in Kerala from the government bulletins and media outlets to generate reusable datasets. This was further visualized as a dashboard through a frontend web application and a JSON repository, which serves as an API for the frontend. RESULTS: From the sourced data, we provided real-time analysis, and daily updates of COVID-19 cases in Kerala, through a user-friendly bilingual dashboard (https://covid19kerala.info/) for non-specialists. To ensure longevity and reusability, the dataset was deposited in an open-access public repository for future analysis. Finally, we provide outbreak trends and demographic characteristics of the individuals affected with COVID-19 in Kerala during the first 138 days of the outbreak. DISCUSSION: We anticipate that our dataset can form the basis for future studies, supplemented with clinical and epidemiological data from the individuals affected with COVID-19 in Kerala. CONCLUSION: We reported a citizen science initiative on the COVID-19 outbreak in Kerala to collect and deposit data in a structured format, which was utilized for visualizing the outbreak trend and describing demographic characteristics of affected individuals.


id: cord-289447-d93qwjui
author: Helmy, Mohamed
date: 2020-10-09
txt: ./txt/cord-289447-d93qwjui.txt
cache: ./cache/cord-289447-d93qwjui.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.mec.2020.e00149
doi: 10.1016/j.mec.2020.e00149
summary:
  Here, we review the latest attempts of combining systems biology and AI in metabolic engineering research, and highlight how this alliance can help overcome the current challenges facing industrial biotechnology, especially for food-related substances and compounds using microorganisms. On the other hand, Jervis et al implemented an ML algorithm to model the bacterial ribosome binding sites (RBSs) sequence-phenotype relationship and accurately predicted the optimal high-producers, an approach that directly apply on wide range of metabolic engineering applications [106] . To understand the key regulatory or emergent bottleneck scenarios that limit their industrial applicability, they undertook a large scale -omics based systems biology approach where they performed time-series proteomics and metabolomics measurements, and analyzed the resultant high-throughput data using statistical analytics and genome-scale modeling. Although genome annotation, both structural and functional, affects most of the biomedical research aspects, it has a special impact on metabolic engineering in general and applications in food industry in particular.

title:
  Systems biology approaches integrated with artificial intelligence for optimized food-focused metabolic engineering

abstract:
  Metabolic engineering aims to maximize the production of bio-economically important substances (compounds, enzymes, or other proteins) through the optimization of the genetics, cellular processes and growth conditions of microorganisms. This requires detailed understanding of underlying metabolic pathways involved in the production of the targeted substances, and how the cellular processes or growth conditions are regulated by the engineering. To achieve this goal, a large system of experimental techniques, compound libraries, computational methods and data resources, including the multi-omics data, are used. The recent advent of multi-omics systems biology approaches significantly impacted the field by opening new avenues to perform dynamic and large-scale analyses that deepen our knowledge on the manipulations. However, with the enormous transcriptomics, proteomics and metabolomics available, it is a daunting task to integrate the data for a more holistic understanding. Novel data mining and analytics approaches, including Artificial Intelligence (AI), can provide breakthroughs where traditional low-throughput experiment-alone methods cannot easily achieve. Here, we review the latest attempts of combining systems biology and AI in metabolic engineering research, and highlight how this alliance can help overcome the current challenges facing industrial biotechnology, especially for food-related substances and compounds using microorganisms.


id: cord-290003-pmf7aps6
title: Assessing sustainable development prospects through remote sensing: A review
author: Avtar, Ram
date: 2020-09-03
txt: ./txt/cord-290003-pmf7aps6.txt
cache: ./cache/cord-290003-pmf7aps6.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S2352938520302974
doi: 10.1016/j.rsase.2020.100402
summary:
  Remote sensing allows for the measurement, integration, and presentation of useful information for effective decision-making at various temporal and spatial scales. Although 50 several approaches and techniques are available to monitor natural resources and hazards, 51 remote sensing (RS) technology has been particularly popular since the 1970s because of its 52 low acquisition costs and high utility for data collection, interpretation, and management. Based on these RS data, forest fragmentation, land use and cover, and species distributions 211 have been mapped and monitored over time (Kerr et al., 2001; Menon and Bawa, 1997) . • Sustainable transportation mapping and analysis in developing countries is 856 greatly affected by the availability, cost, licensing and access to high resolution 857 real-time imageries and image processing software. With the 870 development of new and improved satellite and airborne sensors, data with increasingly 871 higher spatial, spectral, and/or temporal resolution will become available for researchers, decision-making in many areas of sustainable development.

abstract:
  The Earth's ecosystems face severe environmental stress from unsustainable socioeconomic development linked to population growth, urbanization, and industrialization. Governments worldwide are interested in sustainability measures to address these issues. Remote sensing allows for the measurement, integration, and presentation of useful information for effective decision-making at various temporal and spatial scales. Scientists and decision-makers have endorsed extensive use of remote sensing to bridge gaps among disciplines and achieve sustainable development. This paper presents an extensive review of remote sensing technology used to support sustainable development efforts, with a focus on natural resource management and assessment of natural hazards. We further explore how remote sensing can be used in a cross-cutting, interdisciplinary manner to support decision-making aimed at addressing sustainable development challenges. Remote sensing technology has improved significantly in terms of sensor resolution, data acquisition time, and accessibility over the past several years. This technology has also been widely applied to address key issues and challenges in sustainability. Furthermore, an evaluation of the suitability and limitations of various satellite-derived indices proposed in the literature for assessing sustainable development goals showed that these older indices still perform reasonably well. Nevertheless, with advancements in sensor radiometry and resolution, they were less exploited and new indices are less explored.


id: cord-290251-ihq8gdwj
title: A cross-country database of COVID-19 testing
author: Hasell, Joe
date: 2020-10-08
txt: ./txt/cord-290251-ihq8gdwj.txt
cache: ./cache/cord-290251-ihq8gdwj.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1038/s41597-020-00688-8
doi: 10.1038/s41597-020-00688-8
summary:
  The database consists of two parts, provided for each included country: (1) a time series for the cumulative and daily number of tests performed, or people tested, plus derived variables (discussed below); (2) metadata including a detailed description of the source and any available information on data quality or comparability issues needed for the interpretation of the time series. Firstly, for a number of countries, figures reported in official sources -including press releases, government websites, dedicated dashboards, and social media accounts of national authorities -are recorded manually as they are released. The time series for cumulative and daily testing for each country-series is then provided in the covid-testing-all-observations.csv file. In covid-testing-all-observations.csv, for those sources only providing daily testing figures, this field is derived as the running total of the raw daily data, and is also provided per thousand people of the country''s 2020 population.

abstract:
  Our understanding of the evolution of the COVID-19 pandemic is built upon data concerning confirmed cases and deaths. This data, however, can only be meaningfully interpreted alongside an accurate understanding of the extent of virus testing in different countries. This new database brings together official data on the extent of PCR testing over time for 94 countries. We provide a time series for the daily number of tests performed, or people tested, together with metadata describing data quality and comparability issues needed for the interpretation of the time series. The database is updated regularly through a combination of automated scraping and manual collection and verification, and is entirely replicable, with sources provided for each observation. In providing accessible cross-country data on testing output, it aims to facilitate the incorporation of this crucial information into epidemiological studies, as well as track a key component of countries’ responses to COVID-19.


id: cord-291975-y8ck4lo8
title: Robust Estimation of Infection Fatality Rates during the Early Phase of a Pandemic
author: Simon, Perikles
date: 2020-04-10
txt: ./txt/cord-291975-y8ck4lo8.txt
cache: ./cache/cord-291975-y8ck4lo8.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.04.08.20057729
doi: 10.1101/2020.04.08.20057729
summary:
  The estimation of an IFR is based on two different and -regarding the influence of selection biasdivergent procedures to calculate a CFR from infection-related population data. This formula is not relying anymore on cases reported in the official databases of JH or ECDC and it served as a cross-validation figure for the IFR and the CFRs, which are solely based on these data and the population data of Iceland in the validation part of the results section. The IFRdeCode is the figure derived from testing the general population of Iceland and served to cross validate the mortality figures CFR and classic CFR that have been calculated from the data repositories of JH and the IFR that used this repository in conjunction with the test data published by Iceland''s Department of Public Health.

abstract:
  During a pandemic, robust estimation of case fatality rates (CFRs) is essential to plan and control suppression and mitigation strategies. At present, estimates for the CFR of COVID-19 caused by SARS-CoV-2 infection vary considerably. Expert consensus of 0.1-1% covers in practical terms a range from normal seasonable Influenza to Spanish Influenza. In the following, I deduce a formula for an adjusted Infection Fatality Rate (IFR) to assess mortality in a period following a positive test adjusted for selection bias. Official datasets on cases and deaths were combined with data sets on number of tests. After data curation and quality control, a total of IFR (n=819) was calculated for 21 countries for periods of up to 26 days between registration of a case and death. Estimates for IRFs increased with length of period, but levelled off at >9days with a median for all 21 countries of 0.11 (95%-CI: 0.073-0.15). An epidemiologically derived IFR of 0.040 % (95%-CI: 0.029%-0.055%) was determined for Iceland and was very close to the calculated IFR of 0.057% (95%-CI: 0.042-0.078), but 2.7-6-fold lower than CFRs. IFRs, but not CFRs, were positively associated with increased proportions of elderly in age-cohorts (n=21, spearman's ρ =.73, p =.02). Real-time data on molecular and serological testing may further displace classical diagnosis of disease and its related death. I will critically discuss, why, how and under which conditions the IFR, provides a more solid early estimate of the global burden of a pandemic than the CFR.


id: cord-292475-jrl1fowa
author: Abry, Patrice
date: 2020-08-20
txt: ./txt/cord-292475-jrl1fowa.txt
cache: ./cache/cord-292475-jrl1fowa.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32817697/
doi: 10.1371/journal.pone.0237901
summary:
  The novelty of the proposed approach is twofold: 1) the estimation of the reproduction number is achieved by convex optimization within a proximal-based inverse problem formulation, with constraints aimed at promoting piecewise smoothness; 2) the approach is developed in a multivariate setting, allowing for the simultaneous handling of multiple time series attached to different geographical regions, together with a spatial (graph-based) regularization of their evolutions in time. In that spirit, the overarching goal of the present work is twofold: (1) proposing a new, more versatile framework for the estimation of R(t) within the semi-parametric model of [8, 10] , reformulating its estimation as an inverse problem whose functional is minimized by using non smooth proximal-based convex optimization; (2) inserting this approach in an extended multivariate framework, with applications to various complementary datasets corresponding to different geographical regions.

title:
  Spatial and temporal regularization to estimate COVID-19 reproduction number R(t): Promoting piecewise smoothness via convex optimization

abstract:
  Among the different indicators that quantify the spread of an epidemic such as the on-going COVID-19, stands first the reproduction number which measures how many people can be contaminated by an infected person. In order to permit the monitoring of the evolution of this number, a new estimation procedure is proposed here, assuming a well-accepted model for current incidence data, based on past observations. The novelty of the proposed approach is twofold: 1) the estimation of the reproduction number is achieved by convex optimization within a proximal-based inverse problem formulation, with constraints aimed at promoting piecewise smoothness; 2) the approach is developed in a multivariate setting, allowing for the simultaneous handling of multiple time series attached to different geographical regions, together with a spatial (graph-based) regularization of their evolutions in time. The effectiveness of the approach is first supported by simulations, and two main applications to real COVID-19 data are then discussed. The first one refers to the comparative evolution of the reproduction number for a number of countries, while the second one focuses on French departments and their joint analysis, leading to dynamic maps revealing the temporal co-evolution of their reproduction numbers.


id: cord-292835-zzc1a7id
title: An IoT-based Framework for Early Identification and Monitoring of COVID-19 Cases
author: Otoom, Mwaffaq
date: 2020-08-15
txt: ./txt/cord-292835-zzc1a7id.txt
cache: ./cache/cord-292835-zzc1a7id.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32834831/
doi: 10.1016/j.bspc.2020.102149
summary:
  The proposed system would employ an Internet of Things (IoTs) framework to collect real-time symptom data from users to early identify suspected coronaviruses cases, to monitor the treatment response of those who have already recovered from the virus, and to understand the nature of the virus by collecting and analyzing relevant data. To quickly identify potential coronaviruses cases from this real-time symptom data, this work proposes eight machine learning algorithms, namely Support Vector Machine (SVM), Neural Network, Naïve Bayes, K-Nearest Neighbor (K-NN), Decision Table, Decision Stump, OneR, and ZeroR. Based on these results we believe that real-time symptom data would allow these five algorithms to provide effective and accurate identification of potential cases of COVID-19, and the framework would then document the treatment response for each patient who has contracted the virus. The proposed framework consists of five main components: (1) real-time symptom data collection (using wearable devices), (2) treatment and outcome records from quarantine/isolation centers, (3) a data analysis center that uses machine learning algorithms, (4) healthcare physicians, and (5) a cloud infrastructure.

abstract:
  The world has been facing the challenge of COVID-19 since the end of 2019. It is expected that the world will need to battle the COVID-19 pandemic with precautious measures, until an effective vaccine is developed. This paper proposes a real-time COVID-19 detection and monitoring system. The proposed system would employ an Internet of Things (IoTs) framework to collect real-time symptom data from users to early identify suspected coronaviruses cases, to monitor the treatment response of those who have already recovered from the virus, and to understand the nature of the virus by collecting and analyzing relevant data. The framework consists of five main components: Symptom Data Collection and Uploading (using wearable sensors), Quarantine/Isolation Center, Data Analysis Center (that uses machine learning algorithms), Health Physicians, and Cloud Infrastructure. To quickly identify potential coronaviruses cases from this real-time symptom data, this work proposes eight machine learning algorithms, namely Support Vector Machine (SVM), Neural Network, Naïve Bayes, K-Nearest Neighbor (K-NN), Decision Table, Decision Stump, OneR, and ZeroR. An experiment was conducted to test these eight algorithms on a real COVID-19 symptom dataset, after selecting the relevant symptoms. The results show that five of these eight algorithms achieved an accuracy of more than 90%. Based on these results we believe that real-time symptom data would allow these five algorithms to provide effective and accurate identification of potential cases of COVID-19, and the framework would then document the treatment response for each patient who has contracted the virus.


id: cord-295013-ew9n9i7z
title: Field-testing of primary health-care indicators, India
author: Nambiar, Devaki
date: 2020-11-01
txt: ./txt/cord-295013-ew9n9i7z.txt
cache: ./cache/cord-295013-ew9n9i7z.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.2471/blt.19.249565
doi: 10.2471/blt.19.249565
summary:
  [34] [35] [36] Objective To develop a primary health-care monitoring framework and health outcome indicator list, and field-test and triangulate indicators designed to assess health reforms in Kerala, India, 2018-2019. [34] [35] [36] Objective To develop a primary health-care monitoring framework and health outcome indicator list, and field-test and triangulate indicators designed to assess health reforms in Kerala, India, 2018-2019. As already observed in India and other low-and middle-income countries, 29 our results indicate that any approach to improving or monitoring the quality of health-care must be adaptable to local methods of data production and reporting, while ensuring that emerging concerns of local staff are considered. The Every Newborn-BIRTH study was a triangulation of maternal and newborn healthcare data in low-and middle-income countries, 47 and some smaller-scale primary-care indicator triangulation exercises have been undertaken by India''s National Health Systems Resource Centre.

abstract:
  OBJECTIVE: To develop a primary health-care monitoring framework and health outcome indicator list, and field-test and triangulate indicators designed to assess health reforms in Kerala, India, 2018–2019. METHODS: We used a modified Delphi technique to develop a 23-item indicator list to monitor primary health care. We used a multistage cluster random sampling technique to select one district from each of four district clusters, and then select both a family and a primary health centre from each of the four districts. We field-tested and triangulated the indicators using facility data and a population-based household survey. FINDINGS: Our data revealed similarities between facility and survey data for some indicators (e.g. low birth weight and pre-check services), but differences for others (e.g. acute diarrhoeal diseases in children younger than 5 years and blood pressure screening). We made four critical observations: (i) data are available at the facility level but in varying formats; (ii) established global indicators may not always be useful in local monitoring; (iii) operational definitions must be refined; and (iv) triangulation and feedback from the field is vital. CONCLUSION: We observe that, while data can be used to develop indices of progress, interpretation of these indicators requires great care. In the attainment of universal health coverage, we consider that our observations of the utility of certain health indicators will provide valuable insights for practitioners and supervisors in the development of a primary health-care monitoring mechanism.


id: cord-295450-ca7ll1tt
author: Jia, Peng
date: 2020-10-26
txt: ./txt/cord-295450-ca7ll1tt.txt
cache: ./cache/cord-295450-ca7ll1tt.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/33106238/
doi: 10.1136/bmjgh-2020-002925
abstract: nan
summary:
  The outbreak of the COVID-19 has further advanced the demand for an intelligent disease reporting system, also known as the national intelligent syndromic surveillance system (NISSS), 1 which would be able to analyse these suspected cases on the basis of prior knowledge and real-time information before a disease is confirmed clinically and in the laboratory. ► Literature databases containing valuable research findings and knowledge and internet activity data reflecting cyber user awareness should be incorporated into the NISSS in a real-time way for warning or fighting the epidemic. ► The International Institute of Spatial Lifecourse Epidemiology (ISLE), a global health collaborative research network, has committed to working with multiple stakeholders to codevelop the NISSS in China. Such data-sharing mechanisms and infrastructures would also facilitate timely spatial epidemiological research on the basis of individual-level infected cases linked with respective location data from mobile service providers and/or smartphone-based apps without violating confidentiality requirements.

title:
  Early warning of epidemics: towards a national intelligent syndromic surveillance system (NISSS) in China


id: cord-296208-uy1r6lt2
author: Greenspan, Hayit
date: 2020-08-19
txt: ./txt/cord-296208-uy1r6lt2.txt
cache: ./cache/cord-296208-uy1r6lt2.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32890777/
doi: 10.1016/j.media.2020.101800
summary:
  We focus on three specific use-cases for which AI systems can be built: early disease detection, management in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical data. Many studies have emerged in the last several months from the medical imaging community with many research groups as well as companies introducing deep learning based solutions to tackle the various tasks: mostly in detection of the disease (vs normal), and more recently also for staging disease severity. In Section 2 of this paper we focus on three specific use-cases for which AI systems can be built: detection, patient management, and predictive models in which the imaging is combined with additional clinical features. Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection and patient monitoring using deep learning ct image analysis

title:
  Position paper on COVID-19 imaging and AI: from the clinical needs and technological challenges to initial AI solutions at the lab and national level towards a new era for AI in healthcare

abstract:
  In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from clinical requirements to the design of AI-based systems, to the translation of the developed tools to the clinic. We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease at the national level. We focus on three specific use-cases for which AI systems can be built: early disease detection, management in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical data. Infrastructure considerations and population modeling in two European countries will be described. This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead.


id: cord-297811-8gyejoc5
title: EpiJSON: A unified data-format for epidemiology
author: Finnie, Thomas J.R.
date: 2015-12-29
txt: ./txt/cord-297811-8gyejoc5.txt
cache: ./cache/cord-297811-8gyejoc5.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S1755436515000973
doi: 10.1016/j.epidem.2015.12.002
summary:
  We introduce ''EpiJSON'', a new, flexible, and standards-compliant format for the interchange of epidemiological data using JavaScript Object Notation. With this and the common morphology of a dataset in mind, we propose a standard for the storage and transmission of data for infectious disease epidemiology: EpiJSON (Epidemiological JavaScript Object Notation). Fundamentally, the structure of an EpiJSON file consists of three levels that we term "metadata", "records" and "events" (Fig. 2) . An "attribute" object is used for storing unambiguously a discrete piece of information, recording not only the value of the data but also its name, type and units. It provides a variety of functions that can convert data to each of the levels within EpiJSON (metadata, attributes, records, events and objects). In EpiJSON we provide a well-understood file structure with a verifiable format for storing and exchanging epidemiological data.

abstract:
  Epidemiology relies on data but the divergent ways data are recorded and transferred, both within and between outbreaks, and the expanding range of data-types are creating an increasingly complex problem for the discipline. There is a need for a consistent, interpretable and precise way to transfer data while maintaining its fidelity. We introduce ‘EpiJSON’, a new, flexible, and standards-compliant format for the interchange of epidemiological data using JavaScript Object Notation. This format is designed to enable the widest range of epidemiological data to be unambiguously held and transferred between people, software and institutions. In this paper, we provide a full description of the format and a discussion of the design decisions made. We introduce a schema enabling automatic checks of the validity of data stored as EpiJSON, which can serve as a basis for the development of additional tools. In addition, we also present the R package ‘repijson’ which provides conversion tools between this format, line-list data and pre-existing analysis tools. An example is given to illustrate how EpiJSON can be used to store line list data. EpiJSON, designed around modern standards for interchange of information on the internet, is simple to implement, read and check. As such, it provides an ideal new standard for epidemiological, and other, data transfer to the fast-growing open-source platform for the analysis of disease outbreaks.


id: cord-299254-kqpnwkg5
title: INSMA: An integrated system for multimodal data acquisition and analysis in the intensive care unit
author: Sun, Yingcheng
date: 2020-04-28
txt: ./txt/cord-299254-kqpnwkg5.txt
cache: ./cache/cord-299254-kqpnwkg5.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S1532046420300629?v=s5
doi: 10.1016/j.jbi.2020.103434
summary:
  In this paper, we proposed a multimodal data acquisition and analysis system called INSMA, with the ability to acquire, store, process, and visualize multiple types of data from the Philips IntelliVue patient monitor. Enormous volumes of multimodal physiological data are generated including physiological waveform signals, patient monitoring alarm messages, and numerics and if acquired, synchronized and analyzed, this data can been effectively used to support clinical decision-making at the bedside [10, 18] . We have been working on building the Integrated Medical Environment (tIME) [10] to address this critical opportunity and in this paper, we discuss an integrated system (INSMA) that supports multimodal data acquisition, parsing, real-time data analysis and visualization in the ICU. Advances in informatics, whether through data acquisition, physiologic alarm detection, or signal analysis and visualization for decision support have the potential to markedly improve patient treatment in ICUs. Clinical monitors have the ability to collect and visualize important numerics or waveforms, but more work is needed to interface to the monitors and acquire and synchronize multimodal physiological data across a diverse set of clinical devices.

abstract:
  Modern intensive care units (ICU) are equipped with a variety of different medical devices to monitor the physiological status of patients. These devices can generate large amounts of multimodal data daily that include physiological waveform signals (arterial blood pressure, electrocardiogram, respiration), patient alarm messages, numeric vitals data, etc. In order to provide opportunities for increasingly improved patient care, it is necessary to develop an effective data acquisition and analysis system that can assist clinicians and provide decision support at the patient bedside. Previous research has discussed various data collection methods, but a comprehensive solution for bedside data acquisition to analysis has not been achieved. In this paper, we proposed a multimodal data acquisition and analysis system called INSMA, with the ability to acquire, store, process, and visualize multiple types of data from the Philips IntelliVue patient monitor. We also discuss how the acquired data can be used for patient state tracking. INSMA is being tested in the ICU at University Hospitals Cleveland Medical Center.


id: cord-301300-nfl9z8c7
title: Operationalizing and selecting outcome measures for the HEALing Communities Study
author: Slavova, Svetla
date: 2020-10-02
txt: ./txt/cord-301300-nfl9z8c7.txt
cache: ./cache/cord-301300-nfl9z8c7.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.drugalcdep.2020.108328
doi: 10.1016/j.drugalcdep.2020.108328
summary:
  Three secondary outcome measures will support hypothesis testing for specific evidence-based practices known to decrease opioid overdose deaths: (1) number of naloxone units distributed in HCS communities; (2) number of unique HCS residents receiving Food and Drug Administration-approved buprenorphine products for treatment of opioid use disorder; and (3) number of HCS residents with new incidents of high-risk opioid prescribing. The Helping to End Addiction Long-term (HEALing) Communities Study (HCS) is a multisite, parallel-group, cluster randomized wait-list controlled trial evaluating the impact of the Communities That HEAL intervention to reduce opioid overdose deaths and other associated adverse outcomes (Walsh et al., in press) . The research site teams established multiple data use agreements with data owners to support the calculation for more than 80 study measures based on administrative data collections, such as death certificates, emergency medical services data, inpatient and emergency department discharge billing records, Medicaid claims, syndromic surveillance data, PDMP data, Drug Enforcement Administration data on drug take back collection sites and events, DATA 2000 waivered prescriber data, HIV registry, naloxone distribution and dispensed prescription data.

abstract:
  BACKGROUND: The Helping to End Addiction Long-term (HEALing) Communities Study (HCS) is a multisite, parallel-group, cluster randomized wait-list controlled trial evaluating the impact of the Communities That HEAL intervention to reduce opioid overdose deaths and associated adverse outcomes. This paper presents the approach used to define and align administrative data across the four research sites to measure key study outcomes. METHODS: Priority was given to using administrative data and established data collection infrastructure to ensure reliable, timely, and sustainable measures and to harmonize study outcomes across the HCS sites. RESULTS: The research teams established multiple data use agreements and developed technical specifications for more than 80 study measures. The primary outcome, number of opioid overdose deaths, will be measured from death certificate data. Three secondary outcome measures will support hypothesis testing for specific evidence-based practices known to decrease opioid overdose deaths: (1) number of naloxone units distributed in HCS communities; (2) number of unique HCS residents receiving Food and Drug Administration-approved buprenorphine products for treatment of opioid use disorder; and (3) number of HCS residents with new incidents of high-risk opioid prescribing. CONCLUSIONS: The HCS has already made an impact on existing data capacity in the four states. In addition to providing data needed to measure study outcomes, the HCS will provide methodology and tools to facilitate data-driven responses to the opioid epidemic, and establish a central repository for community-level longitudinal data to help researchers and public health practitioners study and understand different aspects of the Communities That HEAL framework.


id: cord-301405-7ijaxk4v
title: Towards Using Graph Analytics for Tracking Covid-19
author: El Mouden, Zakariyaa Ait
date: 2020-12-31
txt: ./txt/cord-301405-7ijaxk4v.txt
cache: ./cache/cord-301405-7ijaxk4v.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S1877050920322961
doi: 10.1016/j.procs.2020.10.029
summary:
  The purpose of this paper is to introduce a graph-based approach of communities detection in the novel coronavirus Covid-19 countries'' datasets. Recent works combined between spectral methods and deep learning models, such as the case of [24] where the authors presented their deep clustering approach to cluster data using both neural networks and graph analytics. Our proposed approach consists of a SC based communities detection where the objective is to have an unsupervised grouping of countries having similar behaviors of Covid-19 spreading. In this paper, we proposed a graph-based approach for clustering Covid-19 data using spectral clustering. Ongoing work intends to link the different processes of the model, developed with two different programming languages (Java and R) to build a model able to cluster heterogeneous data based on graph analytics and spectral clustering for communities'' detection. An application of spectral clustering approach to detect communities in data modeled by graphs

abstract:
  Graph analytics are now considered the state-of-the-art in many applications of communities detection. The combination between the graph’s definition in mathematics and the graphs in computer science as an abstract data structure is the key behind the success of graph-based approaches in machine learning. Based on graphs, several approaches have been developed such as shortest path first (SPF) algorithms, subgraphs extraction, social media analytics, transportation networks, bioinformatic algorithms, etc. While SPF algorithms are widely used in optimization problems, Spectral clustering (SC) algorithms have overcome the limits of the most state-of-art approaches in communities detection. The purpose of this paper is to introduce a graph-based approach of communities detection in the novel coronavirus Covid-19 countries’ datasets. The motivation behind this work is to overcome the limitations of multiclass classification, as SC is an unsupervised clustering algorithm, there is no need to predefine the output clusters as a preprocessing step. Our proposed approach is based on a previous contribution on an automatic estimation of the k number of the output clusters. Based on dynamic statistical data for more than 200 countries, each cluster is supposed to group countries having similar behaviors of Covid-19 propagation.


id: cord-301888-f1drinpl
title: Lancet gate: A matter of fact or a matter of concern
author: Raoult, Didier
date: 2020-09-22
txt: ./txt/cord-301888-f1drinpl.txt
cache: ./cache/cord-301888-f1drinpl.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.nmni.2020.100758
doi: 10.1016/j.nmni.2020.100758
abstract: nan
summary:
  This shows that hic et nunc (here and now), there is not a single 23 truth, but at this stage there are opinions, each one having data that it analyzes in the most 24 appropriate way with the method considered best to answer yes to the hypothesis (3). In fact, the studies reported by the physicians themselves may correct dubious data by their own experience, the computer will 32 not. In practice, under these conditions, nothing is verifiable anymore and a painful 33 experience has just shown us this with the episode of Surgisphere who managed to publish in 34 the two best journals of the medical world, series whose sources are unknown, whose 35 methods are unknown and were retracted. The most extreme case was recently revealed in London, where the 45 most rated restaurant on TripAdvisor called "The Shed at Dulwich" did not exist, and which 46 was, in fact, pure farce fuelled by false comments placed on TripAdvisor.


id: cord-302648-16aq6ai4
author: Iovanovici, Alexandru
date: 2020-09-17
txt: ./txt/cord-302648-16aq6ai4.txt
cache: ./cache/cord-302648-16aq6ai4.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S2352340920312129
doi: 10.1016/j.dib.2020.106318
summary:
  Considering the relative scarcity of real-life traffic data, one can use this data set for micro-simulation during development and validation of Intelligent Transportation Solutions (ITS) algorithms while another facet would be in the area of social and political sciences when discussing the effectiveness and impact of statewide restriction during the COVID19 pandemic. • The main usage of the data, in the field of ITS, is to provide real-life data from a variety of Romanian cities (ranging from small to large in population, area and road network size) useful for training machine learning algorithms for prediction of congestion and for simulation of the impact of traffic incidents over the traffic flow. These are stored into the ./xml.zip archive and follow the naming structure <cityName>_<day>-<month>For a more depth and complete analysis , taking into account the context of the data (the transportation and traffic restrictions imposed on the national level by the SARS-CoV-2/COVID19 pandemic) we present in Table 3 the most important events with impact over the traffic flow.

title:
  A dataset of urban traffic flow for 13 Romanian cities amid lockdown and after ease of COVID19 related restrictions

abstract:
  This dataset comprises street-level traces of traffic flow as reported by Here Maps™ for 13 cities of Romania from 15th. of May 2020 and until 5th. of June 2020. This covers the time two days before lifting of the mobility restrictions imposed by the COVID19 nation-wide State of Emergency and until four days after the second wave of relaxation, announced for 1st. of June 2020. Data were sampled at a 15-minute interval, consistent with the Here API update time. The data are annotated with relevant political decisions and religious events which might influence the traffic flow. Considering the relative scarcity of real-life traffic data, one can use this data set for micro-simulation during development and validation of Intelligent Transportation Solutions (ITS) algorithms while another facet would be in the area of social and political sciences when discussing the effectiveness and impact of statewide restriction during the COVID19 pandemic.


id: cord-303651-fkdep6cp
title: Key questions for modelling COVID-19 exit strategies
author: Thompson, Robin N.
date: 2020-08-12
txt: ./txt/cord-303651-fkdep6cp.txt
cache: ./cache/cord-303651-fkdep6cp.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2006.13012v4.pdf
doi: 10.1098/rspb.2020.1405
summary:
  This leads to a roadmap for future research (figure 1) made up of three key steps: (i) improve estimation of epidemiological parameters using outbreak data from different countries; (ii) understand heterogeneities within and between populations that affect virus transmission and interventions; and (iii) focus on data needs, particularly data collection and methods for planning exit strategies in low-to-middle-income countries (LMICs) where data are often lacking. Three key steps are required: (i) improve estimates of epidemiological parameters (such as the reproduction number and herd immunity fraction) using data from different countries ( §2a-d); (ii) understand heterogeneities within and between populations that affect virus transmission and interventions ( §3a-d); and (iii) focus on data requirements for predicting the effects of individual interventions, particularly-but not exclusively-in data-limited settings such as LMICs ( §4a-c).

abstract:
  Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute ‘Models for an exit strategy’ workshop (11–15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.


id: cord-305542-zyxqcfa3
title: Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle
author: Oliver, Nuria
date: 2020-06-05
txt: ./txt/cord-305542-zyxqcfa3.txt
cache: ./cache/cord-305542-zyxqcfa3.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1126/sciadv.abc0764
doi: 10.1126/sciadv.abc0764
abstract: nan
summary:
  In the following sections, we outline the ways in which different types of mobile phone data can help to better target and design measures to contain and slow the spread of the COVID-19 pandemic. Government and public health authorities broadly raise questions in at least four critical areas of inquiries for which the use of mobile phone data is relevant. Furthermore, around the world, public opinion surveys, social media, and a broad range of civil society actors including consumer groups and human rights organizations have raised legitimate concerns around the ethics, potential loss of privacy, and long-term impact on civil liberties resulting from the use of individual mobile data to monitor COVID-19. Governments should be aware of the value of information and knowledge that can be derived from mobile phone data analysis, especially for monitoring the necessary measures to contain the pandemic.


id: cord-306375-cs4s2o8y
title: COVID-19 surveillance - a descriptive study on data quality issues
author: Costa-Santos, C.
date: 2020-11-05
txt: ./txt/cord-306375-cs4s2o8y.txt
cache: ./cache/cord-306375-cs4s2o8y.txt
extension: .txt
mime: text/plain
url: http://medrxiv.org/cgi/content/short/2020.11.03.20225565v1?rss=1
doi: 10.1101/2020.11.03.20225565
summary:
  Nevertheless, to our knowledge, there is no study performing a structured assessment of data quality issues from the datasets provided by National Surveillance Systems for research purposes during the COVID-19 pandemic. This updated database had an inconsistent manifest, including some variables presented in a different format (for example, instead of a variable with the outcome of the patient, the second dataset presented two dates: death and recovery date), or with different definitions (for example, variable age was defined as the age at the time of COVID-19 onset or as age at the time of COVID-19 notification, in the first and second datasets, respectively), which raised concerns regarding their use for valid research and replication of the analysis made using the first version of data. The DGSAugust dataset included 38520 COVID-19 cases diagnosed between March and June, less 4,003 cases (9%) than the daily public report provided by Portuguese Directorate-General of Health.

abstract:
  Background: High-quality data is crucial for guiding decision making and practicing evidence-based healthcare, especially if previous knowledge is lacking. Nevertheless, data quality frailties have been exposed worldwide during the current COVID-19 pandemic. Focusing on a major Portuguese surveillance dataset, our study aims to assess data quality issues and suggest possible solutions. Methods: On April 27th 2020, the Portuguese Directorate-General of Health (DGS) made available a dataset (DGSApril) for researchers, upon request. On August 4th, an updated dataset (DGSAugust) was also obtained. The quality of data was assessed through analysis of data completeness and consistency between both datasets. Results: DGSAugust has not followed the data format and variables as DGSApril and a significant number of missing data and inconsistencies were found (e.g. 4,075 cases from the DGSApril were apparently not included in DGSAugust). Several variables also showed a low degree of completeness and/or changed their values from one dataset to another (e.g. the variable underlying conditions had more than half of cases showing different information between datasets). There were also significant inconsistencies between the number of cases and deaths due to COVID-19 shown in DGSAugust and by the DGS reports publicly provided daily. Conclusions: The low quality of COVID-19 surveillance datasets limits its usability to inform good decisions and perform useful research. Major improvements in surveillance datasets are therefore urgently needed - e.g. simplification of data entry processes, constant monitoring of data, and increased training and awareness of health care providers - as low data quality may lead to a deficient pandemic control.


id: cord-310406-5pvln91x
author: Asbury, Thomas M
date: 2010-09-02
txt: ./txt/cord-310406-5pvln91x.txt
cache: ./cache/cord-310406-5pvln91x.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/20813045/
doi: 10.1186/1471-2105-11-444
summary:
  RESULTS: We have applied object-oriented technology to develop a downloadable visualization tool, Genome3D, for integrating and displaying epigenomic data within a prescribed three-dimensional physical model of the human genome. In addition, in spite of the many recent efforts to measure and model the genome structure at various resolutions and detail [3] [4] [5] [6] [7] [8] [9] [10] , little work has focused on combining these models into a plausible aggregate, or has taken advantage of the large amount of genomic and epigenomic data available from new high-throughput approaches. The viewer is designed to display data from multiple scales and uses a hierarchical model of the relative positions of all nucleotide atoms in the cell nucleus, i.e., the complete physical genome. An integrated physical genome model can show the interplay between histone modifications and other genomic data, such as SNPs, DNA methylation, the structure of gene, promoter and transcription machinery, etc. In addition to epigenomic data, the physical genome model also provides a platform to visualize highthroughput gene expression data and its interplay with global binding information of transcription factors.

title:
  Genome3D: A viewer-model framework for integrating and visualizing multi-scale epigenomic information within a three-dimensional genome

abstract:
  BACKGROUND: New technologies are enabling the measurement of many types of genomic and epigenomic information at scales ranging from the atomic to nuclear. Much of this new data is increasingly structural in nature, and is often difficult to coordinate with other data sets. There is a legitimate need for integrating and visualizing these disparate data sets to reveal structural relationships not apparent when looking at these data in isolation. RESULTS: We have applied object-oriented technology to develop a downloadable visualization tool, Genome3D, for integrating and displaying epigenomic data within a prescribed three-dimensional physical model of the human genome. In order to integrate and visualize large volume of data, novel statistical and mathematical approaches have been developed to reduce the size of the data. To our knowledge, this is the first such tool developed that can visualize human genome in three-dimension. We describe here the major features of Genome3D and discuss our multi-scale data framework using a representative basic physical model. We then demonstrate many of the issues and benefits of multi-resolution data integration. CONCLUSIONS: Genome3D is a software visualization tool that explores a wide range of structural genomic and epigenetic data. Data from various sources of differing scales can be integrated within a hierarchical framework that is easily adapted to new developments concerning the structure of the physical genome. In addition, our tool has a simple annotation mechanism to incorporate non-structural information. Genome3D is unique is its ability to manipulate large amounts of multi-resolution data from diverse sources to uncover complex and new structural relationships within the genome.


id: cord-312366-8qg1fn8f
title: Mathematical Models for COVID-19 Pandemic: A Comparative Analysis
author: Adiga, Aniruddha
date: 2020-10-30
txt: ./txt/cord-312366-8qg1fn8f.txt
cache: ./cache/cord-312366-8qg1fn8f.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1007/s41745-020-00200-6
doi: 10.1007/s41745-020-00200-6
summary:
  As the pandemic takes hold, researchers begin investigating: (i) various intervention and control strategies; usually pharmaceutical interventions do not work in the event of a pandemic and thus nonpharmaceutical interventions are most appropriate, (ii) forecasting the epidemic incidence rate, hospitalization rate and mortality rate, (iii) efficiently allocating scarce medical resources to treat the patients and (iv) understanding the change in individual and collective behavior and adherence to public policies. Like projection approaches, models for epidemic forecasting can be broadly classified into two broad groups: (i) statistical and machine learning-based data-driven models, (ii) causal or mechanistic models-see 29, 30, 2, 31, 32, 6, 33 and the references therein for the current state of the art in this rapidly evolving field. In the context of COVID-19 case count modeling and forecasting, a multitude of models have been developed based on different assumptions that capture specific aspects of the disease dynamics (reproduction number evolution, contact network construction, etc.).

abstract:
  COVID-19 pandemic represents an unprecedented global health crisis in the last 100 years. Its economic, social and health impact continues to grow and is likely to end up as one of the worst global disasters since the 1918 pandemic and the World Wars. Mathematical models have played an important role in the ongoing crisis; they have been used to inform public policies and have been instrumental in many of the social distancing measures that were instituted worldwide. In this article, we review some of the important mathematical models used to support the ongoing planning and response efforts. These models differ in their use, their mathematical form and their scope.


id: cord-315510-vtt8wvm1
title: Optimizing global food supply chains: The case for blockchain and GSI standards
author: Keogh, John G.
date: 2020-10-16
txt: ./txt/cord-315510-vtt8wvm1.txt
cache: ./cache/cord-315510-vtt8wvm1.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/B9780128189566000178
doi: 10.1016/b978-0-12-818956-6.00017-8
summary:
  This chapter examines the integration of GS1 standards with the functional components of blockchain technology as an approach to realize a coherent standardized framework of industry-based tools for successful food supply chains (FSCs) transformation. A standardized framework will enhance food traceability, drive FSC efficiencies, enable data interoperability, improve data governance practices, and set supply chain identification standards for products and assets (what), exchange parties (who), locations (where), business processes (why), and sequence (when). The technological attributes of Blockchain can combine with smart contracts to enable decentralized and self-organization to create, execute, and manage business transactions (Schaffers, 2018) , creating a landscape for innovative approaches to information and collaborative systems. The adoption of GS1 standards-enabled Blockchain technology has the potential to enable FSC stakeholders to meet the fast-changing needs of the agri-food industry and the evolving regulatory requirements for enhanced traceability and rapid recall of unsafe goods.

abstract:
  This chapter examines the integration of GS1 standards with the functional components of blockchain technology as an approach to realize a coherent standardized framework of industry-based tools for successful food supply chains (FSCs) transformation. The globalization of food systems has engendered significant changes to the operation and structure of FSCs. Alongside increasing consumer demands for safe and sustainable food products, FSCs are challenged with issues related to information transparency and consumer trust. Uncertainty in matters of transparency and trust arises from the growing information asymmetry between food producers and food consumers, in particular, how and where food is cultivated, harvested, processed, and under what conditions. FSCs are tasked with guaranteeing the highest standards in food quality and food safety—ensuring the use of safe and authentic ingredients, limiting product perishability, and mitigating the risk of opportunism, such as quality cheating or falsification of information. A sustainable, food-secure world will require multidirectional sharing of information and enhanced information symmetry between food producers and food consumers. The need for information symmetry will drive transformational changes in FSCs methods of practice and will require a coherent standardized framework of best practice recommendations to manage logistic units in the food chain. A standardized framework will enhance food traceability, drive FSC efficiencies, enable data interoperability, improve data governance practices, and set supply chain identification standards for products and assets (what), exchange parties (who), locations (where), business processes (why), and sequence (when).


id: cord-315531-2gc2dc46
author: McGarvey, Peter B.
date: 2009-09-25
txt: ./txt/cord-315531-2gc2dc46.txt
cache: ./cache/cord-315531-2gc2dc46.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/19779614/
doi: 10.1371/journal.pone.0007162
summary:
  (1) The identification of a hypothetical protein with differential gene and protein expressions in two host systems (mouse macrophage and human HeLa cells) infected by different bacterial (Bacillus anthracis and Salmonella typhimurium) and viral (orthopox) pathogens suggesting that this protein can be prioritized for additional analysis and functional characterization. The centers have generated a heterogeneous set of experimental data using various technologies loosely defined as proteomic, but encompassing genomic, structural, immunology and protein interaction technologies, as well as more standard cell and molecular biology techniques used to validate potential targets identified via high-throughput methods. Here we describe in detail a protein-centric approach for systems integration of such a large and heterogeneous set of data from the NIAID Biodefense Proteomics program, and present scientific case studies to illustrate its application to facilitate the basic understanding of pathogen-host interactions and for the identification of potential candidates for therapeutic or diagnostic targets.

title:
  Systems Integration of Biodefense Omics Data for Analysis of Pathogen-Host Interactions and Identification of Potential Targets

abstract:
  The NIAID (National Institute for Allergy and Infectious Diseases) Biodefense Proteomics program aims to identify targets for potential vaccines, therapeutics, and diagnostics for agents of concern in bioterrorism, including bacterial, parasitic, and viral pathogens. The program includes seven Proteomics Research Centers, generating diverse types of pathogen-host data, including mass spectrometry, microarray transcriptional profiles, protein interactions, protein structures and biological reagents. The Biodefense Resource Center (www.proteomicsresource.org) has developed a bioinformatics framework, employing a protein-centric approach to integrate and support mining and analysis of the large and heterogeneous data. Underlying this approach is a data warehouse with comprehensive protein + gene identifier and name mappings and annotations extracted from over 100 molecular databases. Value-added annotations are provided for key proteins from experimental findings using controlled vocabulary. The availability of pathogen and host omics data in an integrated framework allows global analysis of the data and comparisons across different experiments and organisms, as illustrated in several case studies presented here. (1) The identification of a hypothetical protein with differential gene and protein expressions in two host systems (mouse macrophage and human HeLa cells) infected by different bacterial (Bacillus anthracis and Salmonella typhimurium) and viral (orthopox) pathogens suggesting that this protein can be prioritized for additional analysis and functional characterization. (2) The analysis of a vaccinia-human protein interaction network supplemented with protein accumulation levels led to the identification of human Keratin, type II cytoskeletal 4 protein as a potential therapeutic target. (3) Comparison of complete genomes from pathogenic variants coupled with experimental information on complete proteomes allowed the identification and prioritization of ten potential diagnostic targets from Bacillus anthracis. The integrative analysis across data sets from multiple centers can reveal potential functional significance and hidden relationships between pathogen and host proteins, thereby providing a systems approach to basic understanding of pathogenicity and target identification.


id: cord-315610-ihh521ur
title: KDE Bioscience: Platform for bioinformatics analysis workflows
author: Lu, Qiang
date: 2005-10-11
txt: ./txt/cord-315610-ihh521ur.txt
cache: ./cache/cord-315610-ihh521ur.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/16260186/
doi: 10.1016/j.jbi.2005.09.001
summary:
  KDE Bioscience is a java-based software platform that collects a variety of bioinformatics tools and provides a workflow mechanism to integrate them. In this paper, we present a significant integrative informatics platform, Knowledge Discovery Environment of Bioscience (KDE Bioscience), which is supposed to provide a solution of integration of biological data, algorithms, computing hardware, and biologist intelligence for bioinformatics. Providing biologists with an easyto-use bioinformatics platform requires the integration of sequence and annotation data in different formats from DBMS, flat files, and web pages. KDE Bioscience provides a mechanism for metadata processing that executes before the workflow operates on the actual data. KDE Bioscience has so far collected more than 60 commonly used bioinformatics programs covering the analysis and alignment of nucleotide and protein sequences. KDE Bioscience, which adopts workflow and J2EE, provides an integrative platform for biologists to collaborate and use distributed computing resources in a simple manner.

abstract:
  Bioinformatics is a dynamic research area in which a large number of algorithms and programs have been developed rapidly and independently without much consideration so far of the need for standardization. The lack of such common standards combined with unfriendly interfaces make it difficult for biologists to learn how to use these tools and to translate the data formats from one to another. Consequently, the construction of an integrative bioinformatics platform to facilitate biologists’ research is an urgent and challenging task. KDE Bioscience is a java-based software platform that collects a variety of bioinformatics tools and provides a workflow mechanism to integrate them. Nucleotide and protein sequences from local flat files, web sites, and relational databases can be entered, annotated, and aligned. Several home-made or 3rd-party viewers are built-in to provide visualization of annotations or alignments. KDE Bioscience can also be deployed in client-server mode where simultaneous execution of the same workflow is supported for multiple users. Moreover, workflows can be published as web pages that can be executed from a web browser. The power of KDE Bioscience comes from the integrated algorithms and data sources. With its generic workflow mechanism other novel calculations and simulations can be integrated to augment the current sequence analysis functions. Because of this flexible and extensible architecture, KDE Bioscience makes an ideal integrated informatics environment for future bioinformatics or systems biology research.


id: cord-317602-ftcs7fvq
author: O’Reilly-Shah, Vikas N.
date: 2020-05-12
txt: ./txt/cord-317602-ftcs7fvq.txt
cache: ./cache/cord-317602-ftcs7fvq.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1213/ane.0000000000004945
doi: 10.1213/ane.0000000000004945
abstract: nan
summary:
  Although it appears that there is general consensus on the use of the Substitutable Medical Apps, Reusable Technologies on Fast Healthcare Interoperability Resources (SMART on FHIR) standard developed by the nonprofit Health Level Seven International (HL7) for the interchange of data, the standard is not specific enough to ensure, and regulators have failed to require, that different vendors implement the specification in compatible ways. To briefly recap, if hospitals across the country were able to observe and interpret data being gathered at other institutions in real time and to contribute their own data to the shared repository, the health care system could be learning about and improving its care of COVID-19 patients continuously and collaboratively, based on the sum total of available information rather than incrementally in silos. The public has a pressing interest in ensuring that data standards (eg, OMOP, FHIR) are rapidly developed, adopted by appropriate international standards organizations (eg, HL7), and implemented by EHR vendors in a manner that facilitates interoperability for individual patient care, public health, and research purposes.

title:
  The COVID-19 Pandemic Highlights Shortcomings in US Health Care Informatics Infrastructure: A Call to Action


id: cord-317853-vd35a2eq
title: GISAID: Global initiative on sharing all influenza data – from vision to reality
author: Shu, Yuelong
date: 2017-03-30
txt: ./txt/cord-317853-vd35a2eq.txt
cache: ./cache/cord-317853-vd35a2eq.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.2807/1560-7917.es.2017.22.13.30494
doi: 10.2807/1560-7917.es.2017.22.13.30494
abstract: nan
summary:
  In 2006, the reluctance of data sharing, in particular of avian H5N1 influenza viruses, created an emergency bringing into focus certain limitations and inequities, such that the World Health Organization (WHO)''s Global Influenza Surveillance Network (now the Global Influenza Surveillance and Response System (GISRS) [5] ) was criticised on several fronts, including limited global access to H5N1 sequence data that were stored in a database hosted by the Los Alamos National Laboratories in the United States (US) [6, 7] . Scientists charged with the day to day responsibilities of running WHO Collaborating Centres (CCs) for Influenza, National Influenza Centres and the World Organisation for Animal Health (OIE)/ Food and Agriculture Organization of the United Nations (FAO) [8] reference laboratories, were therefore eager to play a key role and provide scientific oversight in the creation and development of GISAID''s data sharing platform that soon became essential for our work.


id: cord-319828-9ru9lh0c
author: Shi, Shuyun
date: 2020-07-15
txt: ./txt/cord-319828-9ru9lh0c.txt
cache: ./cache/cord-319828-9ru9lh0c.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32834254/
doi: 10.1016/j.cose.2020.101966
summary:
  The potential benefits associated with EHR systems (e.g. public healthcare management, online patient access, and patients medical data sharing) have also attracted the interest of the research community [1, 2, 3, 4, 5, 6, 7, 8, 9] . In theory, EHR systems should ensure the confidentiality, integrity and availability of the stored data, and data can be shared securely among authorized users (e.g. medical practitioners with the right need to access particular patient''s data to facilitate diagno-70 sis). 2. all of data will be exposed once the corresponding symmetric key is lost Table 2 : systems requirements that have been met in Table 1 paper security privacy anonymity integrity authentication controllability auditability accountability [48] designed a system that integrates smart contract with IPFS to improve decentralized cloud storage and controlled data sharing for better user access management. Secure and efficient data accessibility in blockchain based healthcare systems

title:
  Applications of Blockchain in Ensuring the Security and Privacy of Electronic Health Record Systems: A Survey

abstract:
  Due to the popularity of blockchain, there have been many proposed applications of blockchain in the healthcare sector, such as electronic health record (EHR) systems. Therefore, in this paper we perform a systematic literature review of blockchain approaches designed for EHR systems, focusing only on the security and privacy aspects. As part of the review, we introduce relevant background knowledge relating to both EHR systems and blockchain, prior to investigating the (potential) applications of blockchain in EHR systems. We also identify a number of research challenges and opportunities.


id: cord-320040-h8v6cs5b
title: Knowledge sharing during public health emergencies: from global call to effective implementation
author: Delaunay, Sophie
date: 2016-04-01
txt: ./txt/cord-320040-h8v6cs5b.txt
cache: ./cache/cord-320040-h8v6cs5b.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.2471/blt.16.172650
doi: 10.2471/blt.16.172650
abstract: nan
summary:
  To improve epidemic emergency response and to accelerate related research, health authorities in potentially exposed countries must put in place the necessary frameworks for collecting, managing and swiftly making available good-quality, standardized data and for safely securing and sharing biomaterial -such as patient samples -collected during the outbreak. As the Zika outbreak shows, the global public health community is still unprepared to collect good quality, standardized data and biomaterials during emergencies and to share them in ways that provide equitable access to researchers. Together, a virtual biobank and a data repository could provide a global resource for the essential research needed to plan effective outbreak responses. ■ Knowledge sharing during public health emergencies: from global call to effective implementation Sophie Delaunay, a Patricia Kahn, a Mercedes Tatay b & Joanne Liu b


id: cord-324198-b8f99z8r
author: Allam, Zaheer
date: 2020-07-24
txt: ./txt/cord-324198-b8f99z8r.txt
cache: ./cache/cord-324198-b8f99z8r.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/B9780128243138000103
doi: 10.1016/b978-0-12-824313-8.00010-3
summary:
  Besides those, even when countries went on lockdown, the use of technology became even more apparent, as devices such as drones, robots, sensors, smart helmets, and thermal detectors were widely used for different purposes such as delivery, identifying potential coronavirus virus cases and other purposes (WHO, 2020b) . Going further, even post-COVID-19, the role of computation technologies will continue, especially in reevaluating the policy responses, and hence help different stakeholders to identify areas of weakness and how such could be strengthened in case of similar future major disruptive events. According to The World Bank (2020), data transparency not only would help in reducing political tension and win over the coronavirus but is also prerequisite in weathering down the economic shocks affecting the global economy, especially by helping enhancing trust in governments, hence promoting investments especially post-COVID-19. On the Coronavirus (COVID-19) Outbreak and the Smart City Network: Universal Data Sharing Standards Coupled with Artificial Intelligence (AI) to Benefit Urban Health Monitoring and Management

title:
  Underlining the Role of Data Science and Technology in Supporting Supply Chains, Political Stability and Health Networks During Pandemics

abstract:
  This concluding chapter explores how data science and technology has been key in fighting COVID-19 through early detection and in the devising of tools for containing the spread. Interestingly, two precedence constraints are seen to emerge. First, data-driven modeling is the leading policy at an urban and national level, and second, legislations, which are being passed at record speed, will remain as a legacy postvirus. It is expected that those will accelerate the digital transition of communities for decades to come and lead to a resurgence of the smart cities concept which peaked in 2015. This chapter thus outlines the increasing role of data science in health sciences, the need for more robust digital infrastructures, and the role of technology in supporting livability of communities and world order.


id: cord-326908-l9wrrapv
title: Evaluating the Adequacy of Molecular Clock Models Using Posterior Predictive Simulations
author: Duchêne, David A.
date: 2015-07-10
txt: ./txt/cord-326908-l9wrrapv.txt
cache: ./cache/cord-326908-l9wrrapv.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/26163668/
doi: 10.1093/molbev/msv154
summary:
  We test the power of this approach using simulated data and find that the method is sensitive to bias in the estimates of branch lengths, which tends to occur when using underparameterized clock models. 2001) ; uncorrelated beta-distributed rate variation among lineages; misleading node-age priors (i.e., node calibrations that differ considerably from the true node ages); and when data were generated under a strict clock but analyzed with an underparameterized substitution model ( fig. The substitution model was identified as inadequate for the coronavirus data set by the multinomial test statistic estimated using posterior predictive data sets from a clock analysis (P < 0.05); however, it was identified as adequate when using a clock-free method (P = 0.20). In addition, our metric of uncertainty in posterior predictive branch lengths is sensitive to some cases of misspecification of clock models and node-age priors, but not to substitution model misspecification, as shown for our analyses of the coronavirus data set.

abstract:
  Molecular clock models are commonly used to estimate evolutionary rates and timescales from nucleotide sequences. The goal of these models is to account for rate variation among lineages, such that they are assumed to be adequate descriptions of the processes that generated the data. A common approach for selecting a clock model for a data set of interest is to examine a set of candidates and to select the model that provides the best statistical fit. However, this can lead to unreliable estimates if all the candidate models are actually inadequate. For this reason, a method of evaluating absolute model performance is critical. We describe a method that uses posterior predictive simulations to assess the adequacy of clock models. We test the power of this approach using simulated data and find that the method is sensitive to bias in the estimates of branch lengths, which tends to occur when using underparameterized clock models. We also compare the performance of the multinomial test statistic, originally developed to assess the adequacy of substitution models, but find that it has low power in identifying the adequacy of clock models. We illustrate the performance of our method using empirical data sets from coronaviruses, simian immunodeficiency virus, killer whales, and marine turtles. Our results indicate that methods of investigating model adequacy, including the one proposed here, should be routinely used in combination with traditional model selection in evolutionary studies. This will reveal whether a broader range of clock models to be considered in phylogenetic analysis.


id: cord-327651-yzwsqlb2
title: Network inference from multimodal data: A review of approaches from infectious disease transmission
author: Ray, Bisakha
date: 2016-09-06
txt: ./txt/cord-327651-yzwsqlb2.txt
cache: ./cache/cord-327651-yzwsqlb2.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.jbi.2016.09.004
doi: 10.1016/j.jbi.2016.09.004
summary:
  In infectious disease transmission network inference, Bayesian inference frameworks have been primarily used to integrate data such as dates of pathogen sample collection and symptom report date, pathogen genome sequences, and locations of patients [24] [25] [26] . Pathogen genomic data can capture within-host pathogen diversity (the product of effective population size in a generation and the average pathogen replication time [25, 26] ) and dynamics or provide information critical to understanding disease transmission such as evidence of new transmission pathways that cannot be inferred from epidemiological data alone [40, 41] . As molecular epidemiology and infectious disease transmission are areas in which network inference methods have been developed for bringing together multimodal data we use this review to investigate the foundational work in this specific field. In this section we briefly review multimodal integration methods for combining pathogen genomic data and epidemiological data in a single analysis, for inferring infection transmission trees and epidemic dynamic parameters.

abstract:
  Networks inference problems are commonly found in multiple biomedical subfields such as genomics, metagenomics, neuroscience, and epidemiology. Networks are useful for representing a wide range of complex interactions ranging from those between molecular biomarkers, neurons, and microbial communities, to those found in human or animal populations. Recent technological advances have resulted in an increasing amount of healthcare data in multiple modalities, increasing the preponderance of network inference problems. Multi-domain data can now be used to improve the robustness and reliability of recovered networks from unimodal data. For infectious diseases in particular, there is a body of knowledge that has been focused on combining multiple pieces of linked information. Combining or analyzing disparate modalities in concert has demonstrated greater insight into disease transmission than could be obtained from any single modality in isolation. This has been particularly helpful in understanding incidence and transmission at early stages of infections that have pandemic potential. Novel pieces of linked information in the form of spatial, temporal, and other covariates including high-throughput sequence data, clinical visits, social network information, pharmaceutical prescriptions, and clinical symptoms (reported as free-text data) also encourage further investigation of these methods. The purpose of this review is to provide an in-depth analysis of multimodal infectious disease transmission network inference methods with a specific focus on Bayesian inference. We focus on analytical Bayesian inference-based methods as this enables recovering multiple parameters simultaneously, for example, not just the disease transmission network, but also parameters of epidemic dynamics. Our review studies their assumptions, key inference parameters and limitations, and ultimately provides insights about improving future network inference methods in multiple applications.


id: cord-327784-xet20fcw
title: The future of digital health with federated learning
author: Rieke, Nicola
date: 2020-09-14
txt: ./txt/cord-327784-xet20fcw.txt
cache: ./cache/cord-327784-xet20fcw.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/33015372/
doi: 10.1038/s41746-020-00323-1
summary:
  We envision a federated future for digital health and with this perspective paper, we share our consensus view with the aim of providing context and detail for the community regarding the benefits and impact of FL for medical applications (section "Datadriven medicine requires federated efforts"), as well as highlighting key considerations and challenges of implementing FL for digital health (section "Technical considerations"). FL addresses this issue by enabling collaborative learning without centralising data (subsection "The promise of federated efforts") and has already found its way to digital health applications (subsection "Current FL efforts for digital health"). Current FL efforts for digital health Since FL is a general learning paradigm that removes the data pooling requirement for AI model development, the application range of FL spans the whole of AI for healthcare. Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation

abstract:
  Data-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how federated learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.


id: cord-327810-kquh59ry
author: Canhoto, Ana Isabel
date: 2020-10-17
txt: ./txt/cord-327810-kquh59ry.txt
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extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/33100427/
doi: 10.1016/j.jbusres.2020.10.012
summary:
  These requirements mean that financial services organisations are wary of adopting technologies where they lack complete control over use of customer data, or whose workings they do not fully understand, as in the case of black-box type of algorithms. In addition to the specific technical and organisational challenges associated with the specific types of algorithms discussed above, there are some generic issues that condition BANK''s ability to use machine learning in AML profiling. Machine learning''s ability to discover patterns in data, process various types of data and act autonomously promises to enable financial intermediaries to detect money laundering activity in a cost-effective manner (Fernandez, 2019) . While financial services organisations may be essential enablers of money laundering and, indirectly, criminal activity, their perspective is limited to the transaction data for their own customers and their own institution.

title:
  Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective

abstract:
  Financial services organisations facilitate the movement of money worldwide, and keep records of their clients’ identity and financial behaviour. As such, they have been enlisted by governments worldwide to assist with the detection and prevention of money laundering, which is a key tool in the fight to reduce crime and create sustainable economic development, corresponding to Goal 16 of the United Nations Sustainable Development Goals. In this paper, we investigate how the technical and contextual affordances of machine learning algorithms may enable these organisations to accomplish that task. We find that, due to the unavailability of high-quality, large training datasets regarding money laundering methods, there is limited scope for using supervised machine learning. Conversely, it is possible to use reinforced machine learning and, to an extent, unsupervised learning, although only to model unusual financial behaviour, not actual money laundering.


id: cord-328438-irjo0l4s
author: Krittanawong, Chayakrit
date: 2020-10-09
txt: ./txt/cord-328438-irjo0l4s.txt
cache: ./cache/cord-328438-irjo0l4s.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1038/s41569-020-00445-9
doi: 10.1038/s41569-020-00445-9
summary:
  Advances in cardiovascular monitoring technologies, such as the use of ubiquitous mobile devices and the development of novel portable sensors with seamless wireless connectivity and machine learning algorithms that can provide specialist-level diagnosis in near real time, have the potential for a more personalized care. Machine learning is a rapidly developing branch of AI that has shown early promise for use in cardiovascular medicine 61 through the extraction of clinically relevant patterns from complex data, such as detecting myocardial ischaemia from cardiac CT images 62 and interpreting arrhythmias from wearable ECG monitors 33 . Machine learning technology (''deep learning'') 60 has also been shown to improve the performance of shock advice algorithms in an automated external defibrillator 66 to predict the onset of ventricular arrhythmias with the use of an artificial neural network 67 and to predict the onset of sudden cardiac arrest within 72 h by incorporating heart rate variability parameters with vital sign data 68 .

title:
  Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

abstract:
  Ambulatory monitoring is increasingly important for cardiovascular care but is often limited by the unpredictability of cardiovascular events, the intermittent nature of ambulatory monitors and the variable clinical significance of recorded data in patients. Technological advances in computing have led to the introduction of novel physiological biosignals that can increase the frequency at which abnormalities in cardiovascular parameters can be detected, making expert-level, automated diagnosis a reality. However, use of these biosignals for diagnosis also raises numerous concerns related to accuracy and actionability within clinical guidelines, in addition to medico-legal and ethical issues. Analytical methods such as machine learning can potentially increase the accuracy and improve the actionability of device-based diagnoses. Coupled with interoperability of data to widen access to all stakeholders, seamless connectivity (an internet of things) and maintenance of anonymity, this approach could ultimately facilitate near-real-time diagnosis and therapy. These tools are increasingly recognized by regulatory agencies and professional medical societies, but several technical and ethical issues remain. In this Review, we describe the current state of cardiovascular monitoring along the continuum from biosignal acquisition to the identification of novel biosensors and the development of analytical techniques and ultimately to regulatory and ethical issues. Furthermore, we outline new paradigms for cardiovascular monitoring.


id: cord-328826-guqc5866
author: Wissel, Benjamin D
date: 2020-04-25
txt: ./txt/cord-328826-guqc5866.txt
cache: ./cache/cord-328826-guqc5866.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32333753/
doi: 10.1093/jamia/ocaa071
summary:
  MATERIALS AND METHODS: This R Shiny application aggregates data from multiple resources that track COVID-19 and visualizes them through an interactive, online dashboard. It displays COVID-19 data from every county and 188 metropolitan areas in the U.S. Features include rankings of the worst affected areas and auto-generating plots that depict temporal changes in testing capacity, cases, and deaths. Our team developed a methodology to aggregate county-level COVID-19 data into metropolitan areas and display these data in an interactive dashboard that updates in real-time. To track the proportion of each area''s residents that became infected or died of COVID-19, we used the U.S. Census Bureau''s 2019 population estimate for each county to normalize data to tests, cases, and deaths per 10,000 residents. Users can view COVID-19 cases and deaths from The NYT at the county, city, state, or national level, and the total number of tests reported by the COVID Tracking Project, including the breakdown between positive and negative tests, is shown for each state.

title:
  An Interactive Online Dashboard for Tracking COVID-19 in U.S. Counties, Cities, and States in Real Time

abstract:
  OBJECTIVE: To create an online resource that informs the public of COVID-19 outbreaks in their area. MATERIALS AND METHODS: This R Shiny application aggregates data from multiple resources that track COVID-19 and visualizes them through an interactive, online dashboard. RESULTS: The web resource, called the COVID-19 Watcher, can be accessed at https://covid19watcher.research.cchmc.org/. It displays COVID-19 data from every county and 188 metropolitan areas in the U.S. Features include rankings of the worst affected areas and auto-generating plots that depict temporal changes in testing capacity, cases, and deaths. DISCUSSION: The Centers for Disease Control and Prevention (CDC) do not publish COVID-19 data for local municipalities, so it is critical that academic resources fill this void so the public can stay informed. The data used have limitations and likely underestimate the scale of the outbreak. CONCLUSIONS: The COVID-19 Watcher can provide the public with real-time updates of outbreaks in their area.


id: cord-329986-sbyu7yuc
author: Farrokhi, Aydin
date: 2020-11-30
txt: ./txt/cord-329986-sbyu7yuc.txt
cache: ./cache/cord-329986-sbyu7yuc.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0019850120308464
doi: 10.1016/j.indmarman.2020.09.015
summary:
  The study extends the situational crisis communication theory (SCCT) and Attribution theory frameworks built on big data and machine learning capabilities for early detection of crises in the market. This pioneering study is among the first studies that endeavour to use email data and sentiment analysis for extracting meaningful information that helps early detection of a crisis in an organization. This study aims to develop a big data analytics framework by deploying artificial intelligence rational agents generated by R/Python programming language capable of collecting data from different sources, such as emails, Tweets, Facebook, weblogs, online communities, databases, and documents, among others (structured, semistructured, and unstructured data). Previous studies have considered the use of network data for situational awareness; however, to the authors'' knowledge, none have specifically investigated or analyzed the use of email communication by major organizations for situational assessment of a developing crisis.

title:
  Using artificial intelligence to detect crisis related to events: Decision making in B2B by artificial intelligence

abstract:
  Artificial Intelligence (AI) could be an important foundation of competitive advantage in the market for firms. As such, firms use AI to achieve deep market engagement when the firm's data are employed to make informed decisions. This study examines the role of computer-mediated AI agents in detecting crises related to events in a firm. A crisis threatens organizational performance; therefore, a data-driven strategy will result in an efficient and timely reflection, which increases the success of crisis management. The study extends the situational crisis communication theory (SCCT) and Attribution theory frameworks built on big data and machine learning capabilities for early detection of crises in the market. This research proposes a structural model composed of a statistical and sentimental big data analytics approach. The findings of our empirical research suggest that knowledge extracted from day-to-day data communications such as email communications of a firm can lead to the sensing of critical events related to business activities. To test our model, we use a publicly available dataset containing 517,401 items belonging to 150 users, mostly senior managers of Enron during 1999 through the 2001 crisis. The findings suggest that the model is plausible in the early detection of Enron's critical events, which can support decision making in the market.


id: cord-330148-yltc6wpv
title: Trends in the Mechanistic and Dynamic Modeling of Infectious Diseases
author: Lessler, Justin
date: 2016-07-02
txt: ./txt/cord-330148-yltc6wpv.txt
cache: ./cache/cord-330148-yltc6wpv.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32226711/
doi: 10.1007/s40471-016-0078-4
summary:
  Uncertainty was largely addressed through scenario-based approaches (e.g., different future epidemic trajectories were presented for different plausible sets of parameters), and for the most part, different aspects of the transmission dynamics were derived from independent studies, with only the growth rate (i.e., doubling time) estimated from incidence data. These recent attempts to quickly characterize the properties of emerging diseases are emblematic of an increasing focus on developing statistical methods, grounded in dynamical models, to estimate key epidemic parameters based on diverse data sources. High-resolution geographic data can gain additional power when paired with mechanistic models that capture changes in disease risk, as in recent analyses that accounted for the effect of birth, natural infection, and vaccine disruptions driving increases in measles susceptibility and epidemic risk in the wake of the Ebola outbreak [63] . The formal statistical integration of population genetic and epidemic models allows us to estimate the critical epidemiological parameters such as the basic reproductive number directly from pathogen sequence data [75] [76] [77] .

abstract:
  The dynamics of infectious disease epidemics are driven by interactions between individuals with differing disease status (e.g., susceptible, infected, immune). Mechanistic models that capture the dynamics of such “dependent happenings” are a fundamental tool of infectious disease epidemiology. Recent methodological advances combined with access to new data sources and computational power have resulted in an explosion in the use of dynamic models in the analysis of emerging and established infectious diseases. Increasing use of models to inform practical public health decision making has challenged the field to develop new methods to exploit available data and appropriately characterize the uncertainty in the results. Here, we discuss recent advances and areas of active research in the mechanistic and dynamic modeling of infectious disease. We highlight how a growing emphasis on data and inference, novel forecasting methods, and increasing access to “big data” are changing the field of infectious disease dynamics. We showcase the application of these methods in phylodynamic research, which combines mechanistic models with rich sources of molecular data to tie genetic data to population-level disease dynamics. As dynamics and mechanistic modeling methods mature and are increasingly tied to principled statistical approaches, the historic separation between the infectious disease dynamics and “traditional” epidemiologic methods is beginning to erode; this presents new opportunities for cross pollination between fields and novel applications.


