# bib

id: cord-330503-w1m1ci4i
title: IT applications in healthcare management: a survey
author: Yamin, Mohammad
date: 2018-05-31
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url: https://www.ncbi.nlm.nih.gov/pubmed/32289102/
doi: 10.1007/s41870-018-0203-3
summary:
  Advance data transfer and management techniques have made improvements in disease diagnostic and have been a critical role in national health planning and efficient record keeping. In particular, the medical profession has undergone substantial changes through the capabilities of database management, which has given rise to the Healthcare Information Systems (HIS). According to [1] , many programs are developed with the help of AI to perform specific tasks which make use of many activities including medical diagnostic, time sharing, interactive interpreters, graphical user interfaces and the computer mouse, rapid development environments, the linked listdata structure, automatic storage management, symbolic, functional, dynamic, and object-oriented programming. Thus the first phase of the usage of information technology and systems in hospital and healthcare management was to transform paper based records to database systems. AI, Robots, VR, AR, MR, IoMT, ubiquitous medical services, and big data analytics are all directly or indirectly related to IT. Medical internet of things and big data in healthcare

abstract:
  Healthcare management is currently undergoing substantial changes, and reshaping our perception of the medical field. One spectrum is that of the considerable changes that we see in surgical machines and equipment, and the way the procedures are performed. Computing power, Internet and associated technologies are transforming surgical operations into model based procedures. The other spectrum is the management side of healthcare, which is equally critical to the medical profession. In particular, recent advances in the field of Information Technology (IT) is assisting in better management of health appointments and record management. With the proliferation of IT and management, data is now playing a vital role in diagnostics, drug administration and management of healthcare services. With the advancement in data processing, large amounts of medical data collected by medical centres and providers, can now be mined and analysed to assist in planning and making appropriate decisions. In this article, we shall provide an overview of the role of IT that have been reshaping the healthcare management, hospital, health profession and industry.


id: cord-338207-60vrlrim
title: Virus Databases
author: Lefkowitz, E.J.
date: 2008-07-30
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url: https://api.elsevier.com/content/article/pii/B9780123744104007196
doi: 10.1016/b978-012374410-4.00719-6
summary:
  (Each arrow points to the table containing the primary key.) Tables are color-coded according to the source of the information they contain: yellow, data obtained from the original GenBank sequence record and the ICTV Eighth Report; pink, data obtained from automated annotation or manual curation; blue, controlled vocabularies to ensure data consistency; green, administrative data. While most of us store our BLAST search results as files on our desktop computers, it is useful to store this information within the database to provide rapid access to similarity results for comparative purposes; to use these results to assign genes to orthologous families of related sequences; and to use these results in applications that analyze data in the database and, for example, display the results of an analysis between two or more types of viruses showing shared sets of common genes.

abstract:
  As tools and technologies for the analysis of biological organisms (including viruses) have improved, the amount of raw data generated by these technologies has increased exponentially. Today's challenge, therefore, is to provide computational systems that support data storage, retrieval, display, and analysis in a manner that allows the average researcher to mine this information for knowledge pertinent to his or her work. Every article in this encyclopedia contains knowledge that has been derived in part from the analysis of such large data sets, which in turn are directly dependent on the databases that are used to organize this information. Fortunately, continual improvements in data-intensive biological technologies have been matched by the development of computational technologies, including those related to databases. This work forms the basis of many of the technologies that encompass the field of bioinformatics. This article provides an overview of database structure and how that structure supports the storage of biological information. The different types of data associated with the analysis of viruses are discussed, followed by a review of some of the various online databases that store general biological, as well as virus-specific, information.


id: cord-339440-qu913a8q
title: New methods and technologies for enhancing usability and accessibility of educational data
author: Fonseca, David
date: 2020-10-26
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url: https://www.ncbi.nlm.nih.gov/pubmed/33132798/
doi: 10.1007/s10209-020-00765-0
abstract: nan
summary:
  • The invited session entitled "Emerging interactive systems for education", in the thematic area "Learning and This special issue focuses on how to improve universal access to educational data, with emphasis on (a) new technologies and associated data in educational contexts: artificial intelligence systems [70] , robotics [71] [72] [73] , augmented [74] [75] [76] and virtual reality (VR) [77] [78] [79] [80] [81] , and educational data integration and management [82] ; (b) the role of data in the digital transformation and future of higher education: Personal Learning Environments (PLE) [83, 84] , mobile PLE [85, 86] , stealth assessment [87] , technology-supported collaboration and teamwork in educational environments [88] , and student''s engagement and interactions [89, 90] ; (c) user and case studies on ICTs in education [91, 92] ; (d) educational data in serious games and gamification: gamification design [93] [94] [95] [96] , serious game mechanics for education [97, 98] , ubiquitous/pervasive gaming [99] , and game-based learning and teaching programming [100, 101] ; and (e) educational data visualization and data mining [102] : learning analytics [103] , knowledge discovery [104] , user experience [105, 106] , social impact [107] , good practices [108] , and accessibility [109, 110] .


id: cord-339491-lyld3up2
title: Using Machine Learning to assess Covid-19 risks
author: Prakash, A.
date: 2020-06-23
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url: https://doi.org/10.1101/2020.06.23.20137950
doi: 10.1101/2020.06.23.20137950
summary:
  A dataset based on these statistics were generated and was then fed into an unsupervised learning algorithm to reveal patterns and identify similar groups of people in the population. PARTICIPANTS: The adult population were considered for the analysis, development and validation of the model RESULTS: Of 1 million observations generated, 20% of them exhibited Covid symptoms and patterns, and 80% of them belonged to the asymptomatic and non-infected group of people. Using this, our proposed method captures these statistics along with some clinical background and generates a dataset on which we intend to apply an unsupervised learning algorithm to identify patterns and classify them into risk cohorts. Covid based research has evidently increased since the pandemic has struck and related resources are available extensively today, and this method has tried to capture these studies into an interpretable form for analysis and categorization of different risk cohorts that were validated against current data.

abstract:
  ABSTRACT: IMPORTANCE: Identifying potential Covid-19 patients in the general population is a huge challenge at the moment. Given the low availability of infected Covid-19 patients clinical data, it is challenging to understand and comprehend similar and complex patterns in these symptomatic patients. Laboratory testing for Covid19 antigen with RT-PCR | (Reverse Transcriptase) is not possible or economical for whole populations. OBJECTIVE: To develop a Covid risk stratifier model that classifies people into different risk cohorts, based on their symptoms and validate the same. DESIGN: Analysis of Covid cases across Wuhan and New York were done to identify the course of these cases prior to being symptomatic and being hospitalised for the infection. A dataset based on these statistics were generated and was then fed into an unsupervised learning algorithm to reveal patterns and identify similar groups of people in the population. Each of these cohorts were then classified and identified into three risk levels that were validated against the real world cases and studies. SETTING: The study is based on general population. PARTICIPANTS: The adult population were considered for the analysis, development and validation of the model RESULTS: Of 1 million observations generated, 20% of them exhibited Covid symptoms and patterns, and 80% of them belonged to the asymptomatic and non-infected group of people. Upon clustering, three clinically obvious clusters were obtained, out of which the Cluster A had 20% of the symptomatic cases that were classified into one cohort, the other two cohorts, Cluster B had people with no symptoms but with high number of comorbidities and Cluster C had people with few leading indicators for the infection with few comorbidities. This was then validated against 300 participants whose data we collected as a part of a research study through our Covid-research tool and about 92% of them were classified correctly. CONCLUSION: A model was developed and validated that classifies people into Covid risk categories based on their symptoms. This can be used to monitor and track cases that rapidly transition into being symptomatic which eventually get tested positive for the infection in order to initiate early medical interventions. KEYWORDS: Covid-19, Synthetic Data, Patient Clustering, Unsupervised Learning, Risk Classification


id: cord-339886-th1da1bb
title: Towards a genomics-informed, real-time, global pathogen surveillance system
author: Gardy, Jennifer L.
date: 2017-11-13
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url: https://www.ncbi.nlm.nih.gov/pubmed/29129921/
doi: 10.1038/nrg.2017.88
summary:
  Given that outbreaks of emerging infectious diseases (EIDs) most often occur in settings with minimal laboratory capacity, where routine culture and bench-top sequencing are simply not feasible, the need for a portable diagnostic platform capable of in situ clinical metagenomics and outbreak surveillance is evident. Portable genome sequencing technology and digital epidemiology platforms form the foundation for both real-time pathogen and disease surveillance systems and outbreak response efforts, all of which exist within the One Health context, in which surveillance, outbreak detection and response span the human, animal and environmental health domains. For example, genome sequences from a raccoon-associated variant of rabies virus (RRV), when paired with fine-scale geographic information and data from Canadian and US wildlife rabies vaccination programmes, demonstrated that multiple cross-border incursions were responsible for the expansion of RRV into Canada and sustained outbreaks in several provinces 70 ; this finding led to renewed concern about and action against rabies on the part of public health authorities 71 .

abstract:
  The recent Ebola and Zika epidemics demonstrate the need for the continuous surveillance, rapid diagnosis and real-time tracking of emerging infectious diseases. Fast, affordable sequencing of pathogen genomes — now a staple of the public health microbiology laboratory in well-resourced settings — can affect each of these areas. Coupling genomic diagnostics and epidemiology to innovative digital disease detection platforms raises the possibility of an open, global, digital pathogen surveillance system. When informed by a One Health approach, in which human, animal and environmental health are considered together, such a genomics-based system has profound potential to improve public health in settings lacking robust laboratory capacity. SUPPLEMENTARY INFORMATION: The online version of this article (doi:10.1038/nrg.2017.88) contains supplementary material, which is available to authorized users.


id: cord-343944-nm4dx5pq
title: Advances in Visualization Tools for Phylogenomic and Phylodynamic Studies of Viral Diseases
author: Theys, Kristof
date: 2019-08-02
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url: https://www.ncbi.nlm.nih.gov/pubmed/31428595/
doi: 10.3389/fpubh.2019.00208
summary:
  As a first example, we illustrate the development of innovative visualization software packages on the output of a Bayesian phylodynamic analysis of a rabies virus (RABV) data set consisting of time-stamped genetic data along with two discrete trait characteristics per sequence, i.e., the sampling location-in this case the state within the United States from which the sample originated-and the bat host type. Coalescent-based phylodynamic models that connect population genetics theory to genomic data can infer the demographic history of viral populations (65) , and plots of FIGURE 4 | The PhyloGeoTool offers a visual approach to explore large phylogenetic trees and to depict characteristics of strains and clades-including for example the geographic context and distribution of sampling dates-in an interactive way (17) .

abstract:
  Genomic and epidemiological monitoring have become an integral part of our response to emerging and ongoing epidemics of viral infectious diseases. Advances in high-throughput sequencing, including portable genomic sequencing at reduced costs and turnaround time, are paralleled by continuing developments in methodology to infer evolutionary histories (dynamics/patterns) and to identify factors driving viral spread in space and time. The traditionally static nature of visualizing phylogenetic trees that represent these evolutionary relationships/processes has also evolved, albeit perhaps at a slower rate. Advanced visualization tools with increased resolution assist in drawing conclusions from phylogenetic estimates and may even have potential to better inform public health and treatment decisions, but the design (and choice of what analyses are shown) is hindered by the complexity of information embedded within current phylogenetic models and the integration of available meta-data. In this review, we discuss visualization challenges for the interpretation and exploration of reconstructed histories of viral epidemics that arose from increasing volumes of sequence data and the wealth of additional data layers that can be integrated. We focus on solutions that address joint temporal and spatial visualization but also consider what the future may bring in terms of visualization and how this may become of value for the coming era of real-time digital pathogen surveillance, where actionable results and adequate intervention strategies need to be obtained within days.


id: cord-343962-12t247bn
title: Key data for outbreak evaluation: building on the Ebola experience
author: Cori, Anne
date: 2017-05-26
txt: ./txt/cord-343962-12t247bn.txt
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url: https://doi.org/10.1098/rstb.2016.0371
doi: 10.1098/rstb.2016.0371
summary:
  Here we build on experience gained in the West African Ebola epidemic and prior emerging infectious disease outbreaks to set out a checklist of data needed to: (1) quantify severity and transmissibility; (2) characterize heterogeneities in transmission and their determinants; and (3) assess the effectiveness of different interventions. Dynamic transmission models, which account for saturation effects, can be used to assess the long-term impact of the outbreak such as predicting the timing and magnitude of the epidemic peak or the attack rate (final proportion of population infected) [39, 40] . Estimates of the secondary attack rate have been obtained for the West African Ebola epidemic by reconstructing household data based on information reported by cases, in particular, as part of contact-tracing activities [86, 87] . Such data were widely used during the West African Ebola epidemic to quantify the risk of international spread of the disease, and to assess the potential impact of airport screening and travel restrictions on the outbreak [9,94 -96] .

abstract:
  Following the detection of an infectious disease outbreak, rapid epidemiological assessment is critical for guiding an effective public health response. To understand the transmission dynamics and potential impact of an outbreak, several types of data are necessary. Here we build on experience gained in the West African Ebola epidemic and prior emerging infectious disease outbreaks to set out a checklist of data needed to: (1) quantify severity and transmissibility; (2) characterize heterogeneities in transmission and their determinants; and (3) assess the effectiveness of different interventions. We differentiate data needs into individual-level data (e.g. a detailed list of reported cases), exposure data (e.g. identifying where/how cases may have been infected) and population-level data (e.g. size/demographics of the population(s) affected and when/where interventions were implemented). A remarkable amount of individual-level and exposure data was collected during the West African Ebola epidemic, which allowed the assessment of (1) and (2). However, gaps in population-level data (particularly around which interventions were applied when and where) posed challenges to the assessment of (3). Here we highlight recurrent data issues, give practical suggestions for addressing these issues and discuss priorities for improvements in data collection in future outbreaks. This article is part of the themed issue ‘The 2013–2016 West African Ebola epidemic: data, decision-making and disease control’.


id: cord-344152-pb1e2w7s
title: C-ME: A 3D Community-Based, Real-Time Collaboration Tool for Scientific Research and Training
author: Kolatkar, Anand
date: 2008-02-20
txt: ./txt/cord-344152-pb1e2w7s.txt
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url: https://doi.org/10.1371/journal.pone.0001621
doi: 10.1371/journal.pone.0001621
summary:
  Collaborative Molecular Modeling Environment (C-ME) is an interactive community-based collaboration system that allows researchers to organize information, visualize data on a two-dimensional (2-D) or three-dimensional (3-D) basis, and share and manage that information with collaborators in real time. These annotations provide additional information about the atomic structure or image data that can then be evaluated, amended or added to by other project members. For example, protein structure/activity data annotations and images may be kept in paper lab notebooks, manuscripts might be stored electronically in Portable Document Format (PDF), and molecular structure coordinate files may be stored on a hard disk to be viewed and analyzed in graphical molecular viewers, to name a few. Most recently we have developed the Collaborative Molecular Modeling Environment (C-ME), a new collaboratory system that integrates many of the key features available on Kinemage, MICE, iSee, and BioCoRE systems into one thin-client Windows application.

abstract:
  The need for effective collaboration tools is growing as multidisciplinary proteome-wide projects and distributed research teams become more common. The resulting data is often quite disparate, stored in separate locations, and not contextually related. Collaborative Molecular Modeling Environment (C-ME) is an interactive community-based collaboration system that allows researchers to organize information, visualize data on a two-dimensional (2-D) or three-dimensional (3-D) basis, and share and manage that information with collaborators in real time. C-ME stores the information in industry-standard databases that are immediately accessible by appropriate permission within the computer network directory service or anonymously across the internet through the C-ME application or through a web browser. The system addresses two important aspects of collaboration: context and information management. C-ME allows a researcher to use a 3-D atomic structure model or a 2-D image as a contextual basis on which to attach and share annotations to specific atoms or molecules or to specific regions of a 2-D image. These annotations provide additional information about the atomic structure or image data that can then be evaluated, amended or added to by other project members.


id: cord-344307-541hu7so
title: Digital health data-driven approaches to understand human behavior
author: Marsch, Lisa A.
date: 2020-07-12
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url: https://www.ncbi.nlm.nih.gov/pubmed/32653896/
doi: 10.1038/s41386-020-0761-5
summary:
  It provides a synthesis of the scientific literature evaluating how digitally derived empirical data can inform our understanding of health behavior, with a particular focus on understanding the assessment, diagnosis and clinical trajectories of psychiatric disorders. Finally, it concludes with a discussion of future directions and timely opportunities in this line of research and its clinical application, including the development of personalized digital interventions (e.g., behavior change interventions) informed by digital health assessment. Overview of the scientific literature on the application of digitally derived empirical data to understand health behavior and psychopathology A robust and rapidly growing scientific literature is increasingly demonstrating the potential utility of digital assessment in revealing new insights into human behavior, including psychological and psychiatric disorders. And, the real-world precision assessment that digital health methods enable are providing unprecedented insights into human behavior and psychiatric disorders and can inform interventions that are personalizable and adaptive to individuals'' changing needs and preferences over time.

abstract:
  Advances in digital technologies and data analytics have created unparalleled opportunities to assess and modify health behavior and thus accelerate the ability of science to understand and contribute to improved health behavior and health outcomes. Digital health data capture the richness and granularity of individuals’ behavior, the confluence of factors that impact behavior in the moment, and the within-individual evolution of behavior over time. These data may contribute to discovery science by revealing digital markers of health/risk behavior as well as translational science by informing personalized and timely models of intervention delivery. And they may help inform diagnostic classification of clinically problematic behavior and the clinical trajectories of diagnosable disorders over time. This manuscript provides a review of the state of the science of digital health data-driven approaches to understanding human behavior. It reviews methods of digital health assessment and sources of digital health data. It provides a synthesis of the scientific literature evaluating how digitally derived empirical data can inform our understanding of health behavior, with a particular focus on understanding the assessment, diagnosis and clinical trajectories of psychiatric disorders. And, it concludes with a discussion of future directions and timely opportunities in this line of research and its clinical application.


id: cord-346309-hveuq2x9
title: An Epidemiological Network Model for Disease Outbreak Detection
author: Reis, Ben Y
date: 2007-06-26
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url: https://www.ncbi.nlm.nih.gov/pubmed/17593895/
doi: 10.1371/journal.pmed.0040210
summary:
  CONCLUSIONS: The integrated network models of epidemiological data streams and their interrelationships have the potential to improve current surveillance efforts, providing better localized outbreak detection under normal circumstances, as well as more robust performance in the face of shifts in health-care utilization during epidemics and major public events. In order to both improve overall detection performance and reduce vulnerability to baseline shifts, we introduce a general class of epidemiological network models that explicitly capture the relationships among epidemiological data streams. In order to evaluate the practical utility of this approach for surveillance, we constructed epidemiological network models based on real-world historical health-care data and compared their outbreak-detection performance to that of standard historical models. In this study, the researchers developed a new class of surveillance systems called ''''epidemiological network models.'''' These systems aim to improve the detection of disease outbreaks by monitoring fluctuations in the relationships between information detailing the use of various health-care resources over time (data streams).

abstract:
  BACKGROUND: Advanced disease-surveillance systems have been deployed worldwide to provide early detection of infectious disease outbreaks and bioterrorist attacks. New methods that improve the overall detection capabilities of these systems can have a broad practical impact. Furthermore, most current generation surveillance systems are vulnerable to dramatic and unpredictable shifts in the health-care data that they monitor. These shifts can occur during major public events, such as the Olympics, as a result of population surges and public closures. Shifts can also occur during epidemics and pandemics as a result of quarantines, the worried-well flooding emergency departments or, conversely, the public staying away from hospitals for fear of nosocomial infection. Most surveillance systems are not robust to such shifts in health-care utilization, either because they do not adjust baselines and alert-thresholds to new utilization levels, or because the utilization shifts themselves may trigger an alarm. As a result, public-health crises and major public events threaten to undermine health-surveillance systems at the very times they are needed most. METHODS AND FINDINGS: To address this challenge, we introduce a class of epidemiological network models that monitor the relationships among different health-care data streams instead of monitoring the data streams themselves. By extracting the extra information present in the relationships between the data streams, these models have the potential to improve the detection capabilities of a system. Furthermore, the models' relational nature has the potential to increase a system's robustness to unpredictable baseline shifts. We implemented these models and evaluated their effectiveness using historical emergency department data from five hospitals in a single metropolitan area, recorded over a period of 4.5 y by the Automated Epidemiological Geotemporal Integrated Surveillance real-time public health–surveillance system, developed by the Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology on behalf of the Massachusetts Department of Public Health. We performed experiments with semi-synthetic outbreaks of different magnitudes and simulated baseline shifts of different types and magnitudes. The results show that the network models provide better detection of localized outbreaks, and greater robustness to unpredictable shifts than a reference time-series modeling approach. CONCLUSIONS: The integrated network models of epidemiological data streams and their interrelationships have the potential to improve current surveillance efforts, providing better localized outbreak detection under normal circumstances, as well as more robust performance in the face of shifts in health-care utilization during epidemics and major public events.


id: cord-347121-5drl3xas
author: Farah, I.
date: 2020-09-29
txt: ./txt/cord-347121-5drl3xas.txt
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url: https://doi.org/10.1101/2020.09.28.20203257
doi: 10.1101/2020.09.28.20203257
summary:
  The platform combines patient genomic & omics data sets, a marketplace for AI & bioinformatics algorithms, new diagnostic tools, and data-sharing capabilities to advance virus epidemiology and biomarker discovery. The platform is a proven research ecosystem used by universities, biotech, and bioinformatics organizations to share and analyze omics data and can be used for a variety of use cases; from precision medicine, drug discovery, translational science to building data repositories, and tackling a disease outbreak. Our approach is designed to provide healthcare professionals with an urgently needed platform to find and analyze genetic data, and securely and anonymously share sensitive patient data to fight the disease outbreak. Among other use-cases, the provided platform can be used to rapidly study SARS-CoV-2, including analyses of the host response to COVID-19 disease, establish a multi-institutional collaborative datahub for rapid response for current and future pandemics, characterizing potential co-infections, and identifying potential therapeutic targets for preclinical and clinical development.

title:
  A global omics data sharing and analytics marketplace: Case study of a rapid data COVID-19 pandemic response platform.

abstract:
  Under public health emergencies, particularly an early epidemic, it is fundamental that genetic and other healthcare data is shared across borders in both a timely and accurate manner before the outbreak of a global pandemic. However, although the COVID-19 pandemic has created a tidal wave of data, most patient data is siloed, not easily accessible, and due to low sample size, largely not actionable. Based on the precision medicine platform Shivom, a novel and secure data sharing and data analytics marketplace, we developed a versatile pandemic preparedness platform that allows healthcare professionals to rapidly share and analyze genetic data. The platform solves several problems of the global medical and research community, such as siloed data, cross-border data sharing, lack of state-of-the-art analytic tools, GDPR-compliance, and ease-of-use. The platform serves as a central marketplace of 'discoverability'. The platform combines patient genomic & omics data sets, a marketplace for AI & bioinformatics algorithms, new diagnostic tools, and data-sharing capabilities to advance virus epidemiology and biomarker discovery. The bioinformatics marketplace contains some preinstalled COVID-19 pipelines to analyze virus- and host genomes without the need for bioinformatics expertise. The platform will be the quickest way to rapidly gain insight into the association between virus-host interactions and COVID-19 in various populations which can have a significant impact on managing the current pandemic and potential future disease outbreaks.


id: cord-347199-slq70aou
author: Safta, Cosmin
date: 2020-10-07
txt: ./txt/cord-347199-slq70aou.txt
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url: https://doi.org/10.1007/s00466-020-01897-z
doi: 10.1007/s00466-020-01897-z
summary:
  The method is cast as one of Bayesian inference of the latent infection rate (number of people infected per day), conditioned on a time-series of Developing a forecasting method that is applicable in the early epoch of a partially-observed outbreak poses some peculiar difficulties. This infection rate curve is convolved with the Probability Density Function (PDF) of the incubation period of the disease to produce an expression for the time-series of newly symptomatic cases, an observable that is widely reported as "daily new cases" by various data sources [2, 5, 6] . 2, with postulated forms for the infection rate curve and the derivation of the prediction for daily new cases; we also discuss a filtering approach that is applied to the data before using it to infer model parameters.

title:
  Characterization of partially observed epidemics through Bayesian inference: application to COVID-19

abstract:
  We demonstrate a Bayesian method for the “real-time” characterization and forecasting of partially observed COVID-19 epidemic. Characterization is the estimation of infection spread parameters using daily counts of symptomatic patients. The method is designed to help guide medical resource allocation in the early epoch of the outbreak. The estimation problem is posed as one of Bayesian inference and solved using a Markov chain Monte Carlo technique. The data used in this study was sourced before the arrival of the second wave of infection in July 2020. The proposed modeling approach, when applied at the country level, generally provides accurate forecasts at the regional, state and country level. The epidemiological model detected the flattening of the curve in California, after public health measures were instituted. The method also detected different disease dynamics when applied to specific regions of New Mexico.


id: cord-347952-k95wrory
author: Prieto, Diana M
date: 2012-03-30
txt: ./txt/cord-347952-k95wrory.txt
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url: https://doi.org/10.1186/1471-2458-12-251
doi: 10.1186/1471-2458-12-251
summary:
  Conclusions: To adequately address the concerns of the policymakers, we need continuing model enhancements in critical areas including: updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, updating information for contact patterns, adaptation of recent methodologies for collecting human mobility data, and improvement of computational efficiency and accessibility. Conclusions: To adequately address the concerns of the policymakers, we need continuing model enhancements in critical areas including: updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, updating information for contact patterns, adaptation of recent methodologies for collecting human mobility data, and improvement of computational efficiency and accessibility. Of the existing computer simulation models addressing PHP, those focused on disease spread and mitigation of pandemic influenza (PI) have been recognized by the public health officials as useful decision support tools for preparedness planning [1] .

title:
  A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels

abstract:
  BACKGROUND: In recent years, computer simulation models have supported development of pandemic influenza preparedness policies. However, U.S. policymakers have raised several concerns about the practical use of these models. In this review paper, we examine the extent to which the current literature already addresses these concerns and identify means of enhancing the current models for higher operational use. METHODS: We surveyed PubMed and other sources for published research literature on simulation models for influenza pandemic preparedness. We identified 23 models published between 1990 and 2010 that consider single-region (e.g., country, province, city) outbreaks and multi-pronged mitigation strategies. We developed a plan for examination of the literature based on the concerns raised by the policymakers. RESULTS: While examining the concerns about the adequacy and validity of data, we found that though the epidemiological data supporting the models appears to be adequate, it should be validated through as many updates as possible during an outbreak. Demographical data must improve its interfaces for access, retrieval, and translation into model parameters. Regarding the concern about credibility and validity of modeling assumptions, we found that the models often simplify reality to reduce computational burden. Such simplifications may be permissible if they do not interfere with the performance assessment of the mitigation strategies. We also agreed with the concern that social behavior is inadequately represented in pandemic influenza models. Our review showed that the models consider only a few social-behavioral aspects including contact rates, withdrawal from work or school due to symptoms appearance or to care for sick relatives, and compliance to social distancing, vaccination, and antiviral prophylaxis. The concern about the degree of accessibility of the models is palpable, since we found three models that are currently accessible by the public while other models are seeking public accessibility. Policymakers would prefer models scalable to any population size that can be downloadable and operable in personal computers. But scaling models to larger populations would often require computational needs that cannot be handled with personal computers and laptops. As a limitation, we state that some existing models could not be included in our review due to their limited available documentation discussing the choice of relevant parameter values. CONCLUSIONS: To adequately address the concerns of the policymakers, we need continuing model enhancements in critical areas including: updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, updating information for contact patterns, adaptation of recent methodologies for collecting human mobility data, and improvement of computational efficiency and accessibility.


id: cord-348244-1py0k53e
title: Central statistical monitoring of investigator-led clinical trials in oncology
author: Buyse, Marc
date: 2020-06-23
txt: ./txt/cord-348244-1py0k53e.txt
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url: https://doi.org/10.1007/s10147-020-01726-6
doi: 10.1007/s10147-020-01726-6
summary:
  We describe the principles of central statistical monitoring, provide examples of its use, and argue that it could help drive down the cost of randomized clinical trials, especially investigator-led trials, whilst improving their quality. Yet, there is no evidence showing that extensive data monitoring has any major impact on the quality of clinical-trial data, and none of the randomized studies assessing more intensive versus less intensive monitoring has shown any difference in terms of clinically relevant treatment outcomes [18] [19] [20] [21] [22] . Both types of trials may benefit from central statistical monitoring of the data; industry-sponsored trials to target centers that are detected as having potential data quality issues, which may require an on-site audit, and investigatorled trials as the primary method for checking data quality. An evidence-based study of the cost for data monitoring in clinical trials A statistical approach to central monitoring of data quality in clinical trials

abstract:
  Investigator-led clinical trials are pragmatic trials that aim to investigate the benefits and harms of treatments in routine clinical practice. These much-needed trials represent the majority of all trials currently conducted. They are however threatened by the rising costs of clinical research, which are in part due to extensive trial monitoring processes that focus on unimportant details. Risk-based quality management focuses, instead, on “things that really matter”. We discuss the role of central statistical monitoring as part of risk-based quality management. We describe the principles of central statistical monitoring, provide examples of its use, and argue that it could help drive down the cost of randomized clinical trials, especially investigator-led trials, whilst improving their quality.


id: cord-349790-dezauioa
title: Ethical challenges in pathogen sequencing: a systematic scoping review
author: Johnson, Stephanie
date: 2020-06-03
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url: https://www.ncbi.nlm.nih.gov/pubmed/32864469/
doi: 10.12688/wellcomeopenres.15806.1
summary:
  Methods: We systematically searched indexed academic literature from PubMed, Google Scholar, and Web of Science from 2000 to April 2019 for peer-reviewed articles that substantively engaged in discussion of ethical issues in the use of pathogen genome sequencing technologies for diagnostic, surveillance and outbreak investigation. We systematically searched indexed academic literature from PubMed, Google Scholar, and Web of Science from 2000 to April 2019 for peer-reviewed articles that substantively engaged in discussion of ethical issues in the use of pathogen genome sequencing technologies for diagnostic, surveillance and outbreak investigation. Implementation science research may also inform best practices for discussing the meaning and limitations of sequence data and cluster membership with community members and help to identify acceptable and evidence-based approaches that impose the least risk to persons within specific contexts. Many noted that there are important reasons to ensure that the public and individuals understand the uses of data collected as part of a sequencing studies, and the potential risks.

abstract:
  Background: Going forward, the routine implementation of genomic surveillance activities and outbreak investigation is to be expected. We sought to systematically identify the emerging ethical challenges; and to systematically assess the gaps in ethical frameworks or thinking and identify where further work is needed to solve practical challenges. Methods: We systematically searched indexed academic literature from PubMed, Google Scholar, and Web of Science from 2000 to April 2019 for peer-reviewed articles that substantively engaged in discussion of ethical issues in the use of pathogen genome sequencing technologies for diagnostic, surveillance and outbreak investigation. Results: 28 articles were identified; nine United States, five United Kingdom, five The Netherlands, three Canada, two Switzerland, one Australia, two South Africa, and one Italy. Eight articles were specifically about the use of sequencing in HIV. Eleven were not specific to a particular disease. Results were organized into four themes: tensions between public and private interests; difficulties with translation from research to clinical and public health practice; the importance of community trust and support; equity and global partnerships; and the importance of context. Conclusion: While pathogen sequencing has the potential to be transformative for public health, there are a number of key ethical issues that must be addressed, particularly around the conditions of use for pathogen sequence data. Ethical standards should be informed by public values, and further empirical work investigating stakeholders’ views are required. Development in the field should also be under-pinned by a strong commitment to values of justice, in particular global health equity.


id: cord-351065-nyfnwrtm
title: Integrating GIS technique with Google Trends data to analyse COVID-19 severity and public interest
author: Zhang, Tenghao
date: 2020-09-16
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url: https://www.sciencedirect.com/science/article/pii/S0033350620304091?v=s5
doi: 10.1016/j.puhe.2020.09.005
abstract: nan
summary:
  title: Integrating GIS technique with Google Trends data to analyse COVID-19 severity and public interest Some studies suggest that health related issues can cause anxiety which may lead to increased public attention, typically manifested by online information search. Adams et al.''s (2020) GIS-based study points out the shortcomings of using unnormalized COVID-19 demographic data in choropleth mapping, and their use of the normalized data (confirmed cases per 100,000 people) presents a more accurate visualisation of pandemic severity. The COVID-19 case data were retrieved from the US health authority (https://cdc.gov/covid-datatracker). Public interest was captured by people''s Google search data in each state. 7 The data were acquired from the Google Trends service, which uses a normalized relative search volume The role of health anxiety in online health information search The disguised pandemic: The importance of data normalization in COVID-19 web mapping


id: cord-351454-mc7pifep
title: What incentives increase data sharing in health and medical research? A systematic review
author: Rowhani-Farid, Anisa
date: 2017-05-05
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mime: text/plain
url: https://doi.org/10.1186/s41073-017-0028-9
doi: 10.1186/s41073-017-0028-9
summary:
  METHODS: A systematic review (registration: 10.17605/OSF.IO/6PZ5E) of the health and medical research literature was used to uncover any evidence-based incentives, with preand post-empirical data that examined data sharing rates. This review considered published journal articles with empirical data that trialed any incentive to increase data sharing in health and medical research. Articles must have tested an incentive that could increase data sharing in health and medical research. These articles did not fit the inclusion criteria, but based on the abstracts they were mostly concerned with observing data sharing patterns in the health and medical research community, using quantitative and qualitative methods. Given that the systematic review found only one incentive, we classified the data sharing strategies tested in the health and medical research community. This systematic review verified that there are few evidence-based incentives for data sharing in health and medical research.

abstract:
  BACKGROUND: The foundation of health and medical research is data. Data sharing facilitates the progress of research and strengthens science. Data sharing in research is widely discussed in the literature; however, there are seemingly no evidence-based incentives that promote data sharing. METHODS: A systematic review (registration: 10.17605/OSF.IO/6PZ5E) of the health and medical research literature was used to uncover any evidence-based incentives, with pre- and post-empirical data that examined data sharing rates. We were also interested in quantifying and classifying the number of opinion pieces on the importance of incentives, the number observational studies that analysed data sharing rates and practices, and strategies aimed at increasing data sharing rates. RESULTS: Only one incentive (using open data badges) has been tested in health and medical research that examined data sharing rates. The number of opinion pieces (n = 85) out-weighed the number of article-testing strategies (n = 76), and the number of observational studies exceeded them both (n = 106). CONCLUSIONS: Given that data is the foundation of evidence-based health and medical research, it is paradoxical that there is only one evidence-based incentive to promote data sharing. More well-designed studies are needed in order to increase the currently low rates of data sharing. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s41073-017-0028-9) contains supplementary material, which is available to authorized users.


id: cord-351652-y8p3iznq
author: Keogh, John G.
date: 2020-07-10
txt: ./txt/cord-351652-y8p3iznq.txt
cache: ./cache/cord-351652-y8p3iznq.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/B9780128189566000075
doi: 10.1016/b978-0-12-818956-6.00007-5
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 chain transformation. 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. Closely resembling the role and function of the EHR in the healthcare industry, the creation of a Digital Food Record (DFR) is vital for FSCs to facilitate whole-chain traceability, interoperability, linking the different actors and data creators in the chain, and enhancing trust in the market on each product delivered.

title:
  Data and food supply chain: Blockchain and GS1 standards in the food chain: a review of the possibilities and challenges

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 chain transformation. The vulnerability of food supply chains is explored through traceability technologies and standards with particular attention paid to interoperability.


id: cord-352522-qnvgg2e9
title: BioTorrents: A File Sharing Service for Scientific Data
author: Langille, Morgan G. I.
date: 2010-04-14
txt: ./txt/cord-352522-qnvgg2e9.txt
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mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/20418944/
doi: 10.1371/journal.pone.0010071
summary:
  In this study we present BioTorrents, a website that allows open access sharing of scientific data and uses the popular BitTorrent peer-to-peer file sharing technology. A BitTorrent software client (see Table 1 ) uses the data in the torrent file to contact the tracker and allow transferring of the data between computers containing either full or partial copies of the dataset. Information about each dataset on BioTorrents is supplied on a details page giving a description of the data, number of files, date added, user name of the person who created the dataset, and various other details including a link to the actual torrent file. As the number of datasets and users of BioTorrents increases, and to improve on transfer speeds on a geospatial scale (i.e. across countries and continents), we would encourage other institutions to automatically download and share all or some of the data on BioTorrents.

abstract:
  The transfer of scientific data has emerged as a significant challenge, as datasets continue to grow in size and demand for open access sharing increases. Current methods for file transfer do not scale well for large files and can cause long transfer times. In this study we present BioTorrents, a website that allows open access sharing of scientific data and uses the popular BitTorrent peer-to-peer file sharing technology. BioTorrents allows files to be transferred rapidly due to the sharing of bandwidth across multiple institutions and provides more reliable file transfers due to the built-in error checking of the file sharing technology. BioTorrents contains multiple features, including keyword searching, category browsing, RSS feeds, torrent comments, and a discussion forum. BioTorrents is available at http://www.biotorrents.net.


id: cord-354833-vvlsqy36
title: Integrating epitope data into the emerging web of biomedical knowledge resources
author: Peters, Bjoern
date: 2007
txt: ./txt/cord-354833-vvlsqy36.txt
cache: ./cache/cord-354833-vvlsqy36.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/17479127/
doi: 10.1038/nri2092
summary:
  As described in this Innovation article, the Immune Epitope Database and Analysis Resource aims to achieve the same for the more complex and context-dependent information on immune epitopes, and to integrate this data with existing and emerging knowledge resources. With the emergence and consolidation of new databases, this information will expand to include single-nucleotide polymorphisms (SNPs), biomedical imaging and disease association, as well as immune epitope data, such as in the Immune Epitope Database and Analysis Resource (IEDB), which is the focus of this article. We accomplished this by using several hundred different fields encompassing the database, grouped into several main classes or categories, such as the literary reference, the structure of the epitope, the source organism of the epitope and information on the context of epitope recognition, such as the host species, immunization strategy and the type of assay used to detect a response.

abstract:
  The recognition of immune epitopes is an important molecular mechanism of the vertebrate immune system to discriminate between self and non-self. Increasing amounts of data on immune epitopes are becoming available due to technological advances in epitope-mapping techniques and the availability of genomic information for pathogens. Organizing this data poses a challenge that is similar to the successful effort that was required to organize genomic data, which needed the establishment of centralized databases that complement the primary literature to make the data readily accessible and searchable by researchers. As described in this Innovation article, the Immune Epitope Database and Analysis Resource aims to achieve the same for the more complex and context-dependent information on immune epitopes, and to integrate this data with existing and emerging knowledge resources.


id: cord-356353-e6jb0sex
title: Loops, ladders and links: the recursivity of social and machine learning
author: Fourcade, Marion
date: 2020-08-26
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mime: text/plain
url: https://doi.org/10.1007/s11186-020-09409-x
doi: 10.1007/s11186-020-09409-x
summary:
  Both practices rely upon and reinforce a pervasive appetite for digital input or feedback that we characterize as "data hunger." They also share a propensity to assemble insight and make meaning accretively-a propensity that we denote here as "world or meaning accretion." Throughout this article, we probe the dynamic interaction of social and machine learning by drawing examples from one genre of online social contention and connection in which the pervasive influence of machine learning is evident: namely, that which occurs across social media channels and platforms. In such settings, the data accretion upon which machine learning depends for the development of granular insights-and, on social media platforms, associated auctioning and targeting of advertising-compounds the cumulative, sedimentary effect of social data, making negative impressions generated by "revenge porn," or by one''s online identity having been fraudulently coopted, hard to displace or renew.

abstract:
  Machine learning algorithms reshape how people communicate, exchange, and associate; how institutions sort them and slot them into social positions; and how they experience life, down to the most ordinary and intimate aspects. In this article, we draw on examples from the field of social media to review the commonalities, interactions, and contradictions between the dispositions of people and those of machines as they learn from and make sense of each other.


