# pos

id: cord-158219-hk55bzqm
pos: NNP
token:
  https://www.ilfattoquotidiano.it/2020/05/13/coronavirus-nuovo-focolaio-in-molise-72-contagi-a-campobasso-legati-a-un-funerale-della-comunita-rom/5800192/

lemma:
  https://www.ilfattoquotidiano.it/2020/05/13/coronavirus-nuovo-focolaio-in-molise-72-contagi-a-campobasso-legati-a-un-funerale-della-comunita-rom/5800192/


id: cord-326785-le2t1l8g
pos: NN
token:
  dnotaffecttheprevalenceofthethreepnenotypesbutincreasedthefrequencyofmPASposltlvecryptsinanegativebackground(t6

lemma:
  dnotaffecttheprevalenceofthethreepnenotypesbutincreasedthefrequencyofmpasposltlvecryptsinanegativebackground(t6


# ent

id: cord-272085-4mqc8mqd
type: ORG
entity:
  Healthcare Demand Early Estimations of the Impact of General Lockdown to Control the Covid 19 Epidemic


id: cord-012511-fl5llkoj
type: PROTEIN
entity:
  Y21 N3 d31 N4 d41 3 669 000 1 8 3 997 000 0 7 9 402 100 Y12 N1 d12 N2 d22 3 914 000 0 7 3 517 000 1 These 2


id: cord-317093-c70c1op4
type: ORG
entity:
  Bureau of Energy Ministry of Economic Affairs Department of Budget Accounting and Statistics Kaohsiung City Government Statistical


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-102749-tgka0pl0
type: DISEASE
entity:
  enterica Streptococcus pyogenes Escherichia coli Lactobacillus helveticus Lactobacillus delbrueckii Lactobacillus


id: cord-181220-gr29zq1o
type: LAW
entity:
  A Study on The Effectiveness of Lock down Measures to Control The Spread of COVID 19 date 2020 08 09


id: cord-347317-qcghtkk0
type: CELL_TYPE
entity:
  Normal Log normal Weibull Beta Gamma Burr 32 Exponential and Birnbaum Saunders 33 distributions and kept the one resulting in the maximum Log likelihood


id: cord-347317-qcghtkk0
type: CELL_TYPE
entity:
  Normal Log normal Weibull Beta Gamma Burr Exponential and Birnbaum Saunders functions and kept the one yielding the maximum Log likelihood


id: cord-103180-5hkoeca7
type: WORK_OF_ART
entity:
  Universal Screening for Acute HIV Infection in a Routine HIV Counseling and Testing Population Group Testing An Information Theory Perspective Foundations


id: cord-004615-xfi3p601
type: EVENT
entity:
  UK Foot and Mouth Epidemic Dispersal in a Heterogeneous Landscape Modelling and Prediction of Classical Swine Fever Epidemics Quantification of the Transmission of Classical Swine Fever Virus


id: cord-021013-xvc791wx
type: CL
entity:
  stem and root barks in addition All these plant parts and organs have in common that they are important for the actual fitness or for the reproduction and thus the long term survival of the species Spiny species which invest in mechanical defense accumulate fewer alkaloids than soft bodied ones 15 examples are isoquinoline alkaloids


id: cord-021013-xvc791wx
type: WORK_OF_ART
entity:
  Apocynaceae Celestraceae Elaeocarpaceae Euphorbiaceae Fabaceae Orchidaceae Poaceae Ranunculaceae Rhizo


id: cord-021013-xvc791wx
type: ORG
entity:
  Okologische Biochemie Allelochemicals Role in Agriculture and Forestry Cell Culture and Somatic Cell Genetics of Plants F Constabel


id: cord-021013-xvc791wx
type: CHEMICAL
entity:
  quinolizidine alkaloids cryptopleurine haningtonine homohamngtonine haemanthamine isohamngtonine lycorine


id: cord-021013-xvc791wx
type: CHEMICAL
entity:
  glucosinolates nonproteinogenic amino acids cyanogenic glycosides acids aldehydes saponins triterpenes mono


id: cord-261530-vmsq5hhz
type: WORK_OF_ART
entity:
  The Incubation Period of Coronavirus Disease 2019 COVID 19 From Publicly Reported Confirmed Cases Estimation and Application


id: cord-048364-yfn8sy1m
type: GGP
entity:
  household m 3 0 19 m 4 0 036 m 5 0 0037 and m 6 0 00021 and thus the estimate for the total expected number of cases in an infected household is m S 6 i 1 m i 1


id: cord-048364-yfn8sy1m
type: ORG
entity:
  Final Distributions of Infections in Households Estimating Household and Community Transmission Parameters


id: cord-001071-bjx5td52
type: WORK_OF_ART
entity:
  Using Sensor Networks to Study the Effect of Peripatetic Healthcare Workers on the Spread of Hospital Associated Infections


id: cord-344911-pw0ghz3m
type: WORK_OF_ART
entity:
  Multiorgan Failure With Emphasis on Acute Kidney Injury and Severity of COVID 19 Systematic Review and Meta Analysis


id: cord-355201-pjoqahhk
type: ORG
entity:
  Medical Observations Challenges of Treating Adenovirus Infection Application of a Deployable Rapid Assembly Shelter Hospital Novel


id: cord-048446-gaemgm0t
type: ORG
entity:
  Control of Severe Acute Respiratory Syndrome Estimating Individual and Household Reproduction Numbers


id: cord-309378-sfr1x0ob
type: CELL_LINE
entity:
  United States COVID 19 Epidemic Risk Assessment for Georgia Temporal dynamics in viral shedding and transmissibility of COVID 19 Early Transmission Dynamics in Wuhan China


id: cord-309378-sfr1x0ob
type: ORG
entity:
  Pandemic Influenza Prospective Estimation Association of Public Health Interventions With the Epidemiology of the COVID 19 Outbreak


id: cord-309378-sfr1x0ob
type: ORG
entity:
  Early Epidemiological Assessment of the Virulence of Emerging Infectious Diseases A Case Study of an Influenza Pandemic Real Time Estimation of the Risk of Death


id: cord-309378-sfr1x0ob
type: LAW
entity:
  the COVID 19 epidemic Temperature Humidity and Latitude Analysis to Estimate Potential Spread and Seasonality of Coronavirus Disease 2019


id: cord-309378-sfr1x0ob
type: DISEASE
entity:
  influenza A H1N1 pandemic Early Epidemiological Assessment of the Virulence of Emerging Infectious Diseases A Case Study of an Influenza Pandemic Real Time Estimation of the Risk


id: cord-314211-tv1nhojk
type: EVENT
entity:
  the Early COVID 19 Outbreak Retrospective Observational Infoveillance Study Big data integration and analytics


id: cord-331375-tbuijeje
type: DISEASE
entity:
  influenza A H1N1 pandemic Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome


id: cord-300930-47a4pu27
type: CHEMICAL
entity:
  _1_ _________________________________________________________________________ Figure 1 The Magic Trick The answer is 00101 5 CC


id: cord-326785-le2t1l8g
type: DNA
entity:
  7 nan small cell carcinomas 4 adenocarcmamas 3 squamous cell carcinomas 3 bronchiolaalveolar cell carcinomas 2 8 cell lymphomas 1 small cell carcinoma 1 atypical camnoid and 1 metastatic breast Carcmoma


id: cord-326785-le2t1l8g
type: PROTEIN
entity:
  J Qulnn N A Athanasau Nufield Depanment of Paihology and Bactenotogy Level 4 John RadcOffe Hospital Oxford


id: cord-326785-le2t1l8g
type: PROTEIN
entity:
  cell adhesion molecules ICAM t interCeIIUlar adhesion molecule 1 ELAM t lendothelial leucocyte adhesion molecule t


id: cord-326785-le2t1l8g
type: GGP
entity:
  Cand37 C werenegativeforAcPaswerealltissuesfixedinethana1 lnformalcalciumfixedtissueand formic acidlsodium citrate


id: cord-326785-le2t1l8g
type: GGP
entity:
  eXtinCliOn 01 cla s cal diagnostic surgical pathology but so far the reports of the death of our specialty have been grossly exaggerated Indeed the newtechnologies of the 1990sare seenas aids torathet


id: cord-326785-le2t1l8g
type: ORG
entity:
  Usingthistechniqueitisnowpossibletocombinehighresoiutioniightmicroscopywithaprecise immunocytochemicai


# url

id: cord-158219-hk55bzqm
domain: www.ilfattoquotidiano.it
url:
  http://www.ilfattoquotidiano.it/2020/05/13/coronavirus-nuovo-focolaio-in-molise-72-contagi-a-campobasso-legati-a-un-funerale-della-comunita-rom/5800192/


# bib

id: cord-001071-bjx5td52
author: Vanhems, Philippe
date: 2013-09-11
txt: ./txt/cord-001071-bjx5td52.txt
cache: ./cache/cord-001071-bjx5td52.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3770639/
doi: 10.1371/journal.pone.0073970
summary:
  The number and duration of contacts varied between mornings, afternoons and nights, and contact matrices describing the mixing patterns between HCW and patients were built for each time period. The collected data can provide information on important aspects that impact the spreading patterns of infectious diseases, such as the strong heterogeneity of contact numbers and durations across individuals, the variability in the number of contacts during a day, and the fraction of repeated contacts across days. In particular, wearable sensors based on active Radio-Frequency IDentification (RFID) technology have been used to measure face-to-face proximity relations between individuals with a high spatio-temporal resolution in various contexts [17] that include social gatherings [18, 19] , schools [20, 21] and hospitals [22, 23] . In this paper we report on the use of wearable proximity sensors [17] to measure the numbers and durations of contacts between individuals in an acute care geriatric unit of a university hospital.

title:
  Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors

abstract:
  BACKGROUND: Contacts between patients, patients and health care workers (HCWs) and among HCWs represent one of the important routes of transmission of hospital-acquired infections (HAI). A detailed description and quantification of contacts in hospitals provides key information for HAIs epidemiology and for the design and validation of control measures. METHODS AND FINDINGS: We used wearable sensors to detect close-range interactions (“contacts”) between individuals in the geriatric unit of a university hospital. Contact events were measured with a spatial resolution of about 1.5 meters and a temporal resolution of 20 seconds. The study included 46 HCWs and 29 patients and lasted for 4 days and 4 nights. 14,037 contacts were recorded overall, 94.1% of which during daytime. The number and duration of contacts varied between mornings, afternoons and nights, and contact matrices describing the mixing patterns between HCW and patients were built for each time period. Contact patterns were qualitatively similar from one day to the next. 38% of the contacts occurred between pairs of HCWs and 6 HCWs accounted for 42% of all the contacts including at least one patient, suggesting a population of individuals who could potentially act as super-spreaders. CONCLUSIONS: Wearable sensors represent a novel tool for the measurement of contact patterns in hospitals. The collected data can provide information on important aspects that impact the spreading patterns of infectious diseases, such as the strong heterogeneity of contact numbers and durations across individuals, the variability in the number of contacts during a day, and the fraction of repeated contacts across days. This variability is however associated with a marked statistical stability of contact and mixing patterns across days. Our results highlight the need for such measurement efforts in order to correctly inform mathematical models of HAIs and use them to inform the design and evaluation of prevention strategies.


id: cord-004615-xfi3p601
title: A branching model for the spread of infectious animal diseases in varying environments
author: Trapman, Pieter
date: 2004-03-03
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7080114/
doi: 10.1007/s00285-004-0267-5
abstract:
  This paper is concerned with a stochastic model, describing outbreaks of infectious diseases that have potentially great animal or human health consequences, and which can result in such severe economic losses that immediate sets of measures need to be taken to curb the spread. During an outbreak of such a disease, the environment that the infectious agent experiences is therefore changing due to the subsequent control measures taken. In our model, we introduce a general branching process in a changing (but not random) environment. With this branching process, we estimate the probability of extinction and the expected number of infected individuals for different control measures. We also use this branching process to calculate the generating function of the number of infected individuals at any given moment. The model and methods are designed using important infections of farmed animals, such as classical swine fever, foot-and-mouth disease and avian influenza as motivating examples, but have a wider application, for example to emerging human infections that lead to strict quarantine of cases and suspected cases (e.g. SARS) and contact and movement restrictions.


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-012511-fl5llkoj
title: Standardizing Scenarios to Assess the Need to Respond to an Influenza Pandemic
author: Meltzer, Martin I.
date: 2015-05-01
txt: ./txt/cord-012511-fl5llkoj.txt
cache: ./cache/cord-012511-fl5llkoj.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4481578/
doi: 10.1093/cid/civ088
abstract: nan
summary:
  We were tasked to evaluate the 6 following interventions: invasive mechanical ventilators, influenza antiviral drugs for treatment (but not large-scale prophylaxis), influenza vaccines, respiratory protective devices for healthcare workers and surgical face masks for patients, school closings to reduce transmission, and airport-based screening to identify those ill with novel influenza virus entering the United States. To allow easy comparison between results (a specification), we standardized a risk space defined by using ranges of transmission and clinical severity from a previously published influenza severity assessment framework ( Figure 1 ) [5] . Standardized epidemiological curves-contact matrix: To model the 4 epidemic curves (Figure 2 ), we built a simple, nonprobabilistic (ie, deterministic) model in which we divided the population into 4 age groups (0-10, 11-20, 21-60, ≥61 years).


id: cord-021013-xvc791wx
title: Chapter 1 Allelochemical Properties or the Raison D''être of Alkaloids
author: Wink, Michael
date: 2008-05-30
txt: ./txt/cord-021013-xvc791wx.txt
cache: ./cache/cord-021013-xvc791wx.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148816/
doi: 10.1016/s0099-9598(08)60134-0
summary:
  In animals, we can observe the analogous situation in that many insects and other invertebrates (especially those which are sessile and unprotected by armor), but also some vertebrates, store secondary metabolites for their defense which are often similar in structure to plant allelochemicals (1,4,12,16,17,28-30, [494] [495] [496] 503) . During the next three decades this concept was improved experimentally, and we can summarize the present situation as follows Although the biological function of many plant-derived secondary metabolites has not been studied experimentally, it is now generally assumed that these compounds are important for the survival and fitness of a plant and that they are not useless waste products, as was suggested earlier in the twentieth century (34, 35) . These "generalists," as we can also call this subgroup of herbivores, are usually deterred from feeding on plants which store especially noxious metabolites and select those with less active ones (such as our crop species, where man has bred away many of the secondary metabolites that were originally present; see Table XI ).

abstract:
  This chapter provides evidence that alkaloids are not waste products or functionless molecules as formerly assumed, but rather defense compounds employed by plants for survival against herbivores and against microorganisms and competing plants. These molecules were developed during evolution through natural selection in that they fit many important molecular targets, often receptors, of cells, which are seen in molecules that mimic endogenous neurotransmitters. The chapter discusses that microorganisms and herbivores rely on plants as a food source. Since both have survived, there must be mechanisms of adaptations toward the defensive chemistry of plants. Many herbivores have evolved strategies to avoid the extremely toxic plants and prefer the less toxic ones. Many herbivores have potent mechanisms to detoxify xenobiotics, which allow the exploitation of at least the less toxic plants. In insects, many specialists evolved that are adapted to the defense chemicals of their host plant, in that they accumulate these compounds and exploit them for their own defense. Alkaloids function as defense molecules against insect predators in the examples studied, and this is further support for the hypothesis that the same compound also serves for chemical defense in the host plant. It needs more experimental data to understand fully the intricate interconnections between plants, their alkaloids, and herbivores, microorganisms, and other plants.


id: cord-048364-yfn8sy1m
title: Estimating Individual and Household Reproduction Numbers in an Emerging Epidemic
author: Fraser, Christophe
date: 2007-08-22
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1950082/
doi: 10.1371/journal.pone.0000758
abstract:
  Reproduction numbers, defined as averages of the number of people infected by a typical case, play a central role in tracking infectious disease outbreaks. The aim of this paper is to develop methods for estimating reproduction numbers which are simple enough that they could be applied with limited data or in real time during an outbreak. I present a new estimator for the individual reproduction number, which describes the state of the epidemic at a point in time rather than tracking individuals over time, and discuss some potential benefits. Then, to capture more of the detail that micro-simulations have shown is important in outbreak dynamics, I analyse a model of transmission within and between households, and develop a method to estimate the household reproduction number, defined as the number of households infected by each infected household. This method is validated by numerical simulations of the spread of influenza and measles using historical data, and estimates are obtained for would-be emerging epidemics of these viruses. I argue that the household reproduction number is useful in assessing the impact of measures that target the household for isolation, quarantine, vaccination or prophylactic treatment, and measures such as social distancing and school or workplace closures which limit between-household transmission, all of which play a key role in current thinking on future infectious disease mitigation.


id: cord-048446-gaemgm0t
title: Transmissibility of the Influenza Virus in the 1918 Pandemic
author: White, Laura Forsberg
date: 2008-01-30
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2204055/
doi: 10.1371/journal.pone.0001498
abstract:
  BACKGROUND: With a heightened increase in concern for an influenza pandemic we sought to better understand the 1918 Influenza pandemic, the most devastating epidemic of the previous century. METHODOLOGY/PRINCIPAL FINDINGS: We use data from several communities in Maryland, USA as well as two ships that experienced well-documented outbreaks of influenza in 1918. Using a likelihood-based method and a nonparametric method, we estimate the serial interval and reproductive number throughout the course of each outbreak. This analysis shows the basic reproductive number to be slightly lower in the Maryland communities (between 1.34 and 3.21) than for the enclosed populations on the ships (R(0) = 4.97, SE = 3.31). Additionally the effective reproductive number declined to sub epidemic levels more quickly on the ships (within around 10 days) than in the communities (within 30–40 days). The mean serial interval for the ships was consistent (3.33, SE = 5.96 and 3.81, SE = 3.69), while the serial intervals in the communities varied substantially (between 2.83, SE = 0.53 and 8.28, SE = 951.95). CONCLUSIONS/SIGNIFICANCE: These results illustrate the importance of considering the population dynamics when making statements about the epidemiological parameters of Influenza. The methods that we employ for estimation of the reproductive numbers and the serial interval can be easily replicated in other populations and with other diseases.


id: cord-102749-tgka0pl0
title: Taxonomic classification method for metagenomics based on core protein families with Core-Kaiju
author: Tovo, Anna
date: 2020-05-01
txt: ./txt/cord-102749-tgka0pl0.txt
cache: ./cache/cord-102749-tgka0pl0.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.01.08.898395
doi: 10.1101/2020.01.08.898395
summary:
  In this study, we first apply and compare different bioinformatics methods based on 16S ribosomal RNA gene and whole genome shotgun sequencing for taxonomic classification to three small mock communities of bacteria, of which the compositions are known. In particular, we propose an updated version of Kaiju, which combines the power of shotgun metagenomics data with a more focused marker gene classification method, similar to 16S rRNA, but based on core protein domain families (40, 41, 42, 43) from the PFAM database (44) . As shown in (27) , where different amplicon sequencing methods are tested on both simulated and real data and the results are compared to those obtained with metagenomic pipelines, the whole genome approach resulted to outperform the previous ones in terms of both number of identified strains, taxonomic and functional resolution and reliability on estimates of microbial relative abundance distribution in samples.

abstract:
  Characterizing species diversity and composition of bacteria hosted by biota is revolutionizing our understanding of the role of symbiotic interactions in ecosystems. However, determining microbiomes diversity implies the classification of taxa composition within the sampled community, which is often done via the assignment of individual reads to taxa by comparison to reference databases. Although computational methods aimed at identifying the microbe(s) taxa are available, it is well known that inferences using different methods can vary widely depending on various biases. In this study, we first apply and compare different bioinformatics methods based on 16S ribosomal RNA gene and whole genome shotgun sequencing for taxonomic classification to three small mock communities of bacteria, of which the compositions are known. We show that none of these methods can infer both the true number of taxa and their abundances. We thus propose a novel approach, named Core-Kaiju, which combines the power of shotgun metagenomics data with a more focused marker gene classification method similar to 16S, but based on emergent statistics of core protein domain families. We thus test the proposed method on the three small mock communities and also on medium- and highly complex mock community datasets taken from the Critical Assessment of Metagenome Interpretation challenge. We show that Core-Kaiju reliably predicts both number of taxa and abundance of the analysed mock bacterial communities. Finally we apply our method on human gut samples, showing how Core-Kaiju may give more accurate ecological characterization and fresh view on real microbiomes.


id: cord-103180-5hkoeca7
title: Sample pooling methods for efficient pathogen screening: Practical implications
author: Furstenau, Tara N.
date: 2020-07-16
txt: ./txt/cord-103180-5hkoeca7.txt
cache: ./cache/cord-103180-5hkoeca7.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.07.16.206060
doi: 10.1101/2020.07.16.206060
summary:
  Sample pooling methods improve the efficiency of large-scale pathogen screening campaigns by reducing the number of tests and reagents required to accurately categorize positive and negative individuals. Here we use computational simulations to determine how several theoretical approaches compare in terms of (a) the number of tests, to minimize costs and save reagents, (b) the number of sequential steps, to reduce the time it takes to complete the assay, (c) the number of samples per pool, to avoid the limits of detection, (d) simplicity, to reduce the risk of human error, and (e) robustness, to poor estimates of the number of positive samples. 25 Due to practical concerns, Dorfman''s group testing approach was never applied to 26 syphilis screening because the large number of negative samples had a tendency to 27 dilute the antigen in positive samples below the level of detection [6] .

abstract:
  Due to the large number of negative tests, individually screening large populations for rare pathogens can be wasteful and expensive. Sample pooling methods improve the efficiency of large-scale pathogen screening campaigns by reducing the number of tests and reagents required to accurately categorize positive and negative individuals. Such methods rely on group testing theory which mainly focuses on minimizing the total number of tests; however, many other practical concerns and tradeoffs must be considered when choosing an appropriate method for a given set of circumstances. Here we use computational simulations to determine how several theoretical approaches compare in terms of (a) the number of tests, to minimize costs and save reagents, (b) the number of sequential steps, to reduce the time it takes to complete the assay, (c) the number of samples per pool, to avoid the limits of detection, (d) simplicity, to reduce the risk of human error, and (e) robustness, to poor estimates of the number of positive samples. We found that established methods often perform very well in one area but very poorly in others. Therefore, we introduce and validate a new method which performs fairly well across each of the above criteria making it a good general use approach.


id: cord-103342-stqj3ue5
title: A minimal and adaptive prediction strategy for critical resource planning in a pandemic
author: Prakash, Meher K
date: 2020-04-10
txt: ./txt/cord-103342-stqj3ue5.txt
cache: ./cache/cord-103342-stqj3ue5.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.04.08.20057414
doi: 10.1101/2020.04.08.20057414
summary:
  We propose a strategy for estimating the number of infections and the number of deaths, that does away with time-series modeling, and instead makes use of a ''phase portrait approach''. Using our model, we predict the number of infections and deaths in Italy and New York State, based on an adaptive algorithm which uses early available data, and show that our predictions closely match the actual outcomes. Our approach can be summarized as follows: The COVID-19 data from most countries suggests that, especially in the growing phase of the pandemic, the number of active cases and the number of hospitalizations are both proportional to the total number of infections: approximately around 70-90 % and 20-30%, respectively. Thus, using the data from South Korea as a reference standard, the deaths versus infections curve has been readjusted as seen in Figure:3A CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

abstract:
  Current epidemiological models can in principle model the temporal evolution of a pandemic. However, any such model will rely on parameters that are unknown, which in practice are estimated using stochastic and poorly measured quantities. As a result, an early prediction of the long-term evolution of a pandemic will quickly lose relevance, while a late model will be too late to be useful for disaster management. Unless a model is designed to be adaptive, it is bound either to lose relevance over time, or lose trust and thus not have a second-chance for retraining. We propose a strategy for estimating the number of infections and the number of deaths, that does away with time-series modeling, and instead makes use of a 'phase portrait approach'. We demonstrate that, with this approach, there is a universality to the evolution of the disease across countries, that can then be usedto make reliable predictions. These same models can also be used to plan the requirements for critical resources during the pandemic. The approach is designed for simplicity of interpretation, and adaptivity over time. Using our model, we predict the number of infections and deaths in Italy and New York State, based on an adaptive algorithm which uses early available data, and show that our predictions closely match the actual outcomes. We also carry out a similar exercise for India, where in addition to projecting the number of infections and deaths, we also project the expected range of critical resource requirements for hospitalizations in a location.


id: cord-132307-bkkzg6h1
author: Blanco, Natalia
date: 2020-07-06
url: https://arxiv.org/pdf/2007.02712v1.pdf
doi: nan
title:
  Prospective Prediction of Future SARS-CoV-2 Infections Using Empirical Data on a National Level to Gauge Response Effectiveness

abstract:
  Predicting an accurate expected number of future COVID-19 cases is essential to properly evaluate the effectiveness of any treatment or preventive measure. This study aimed to identify the most appropriate mathematical model to prospectively predict the expected number of cases without any intervention. The total number of cases for the COVID-19 epidemic in 28 countries was analyzed and fitted to several simple rate models including the logistic, Gompertz, quadratic, simple square, and simple exponential growth models. The resulting model parameters were used to extrapolate predictions for more recent data. While the Gompertz growth models (mean R2 = 0.998) best fitted the current data, uncertainties in the eventual case limit made future predictions with logistic models prone to errors. Of the other models, the quadratic rate model (mean R2 = 0.992) fitted the current data best for 25 (89 %) countries as determined by R2 values. The simple square and quadratic models accurately predicted the number of future total cases 37 and 36 days in advance respectively, compared to only 15 days for the simple exponential model. The simple exponential model significantly overpredicted the total number of future cases while the quadratic and simple square models did not. These results demonstrated that accurate future predictions of the case load in a given country can be made significantly in advance without the need for complicated models of population behavior and generate a reliable assessment of the efficacy of current prescriptive measures against disease spread.


id: cord-151183-o06mwd4d
title: Projected Development of COVID-19 in Louisiana
author: Tam, Ka-Ming
date: 2020-04-06
txt: ./txt/cord-151183-o06mwd4d.txt
cache: ./cache/cord-151183-o06mwd4d.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2004.02859v1.pdf
doi: nan
summary:
  While the Susceptible-Infected-Recovered (SIR) model may well describe the dynamics of the spreading 1,2 , accurate predictions rely on knowing the number of confirmed cases, which is severely hampered by the limitations of testing. Combining this information with the mortality rate can be a better strategy to predict the number of cases than relying on the con-firmed infection count alone. The exponential growth of the number of fatalities at the beginning of the epidemic should represent the spreading of COVID-19 reasonably well since the mechanisms for slowing the dynamics, such as improved detection and social distancing, are delayed in time By fitting the available fatalities data (see Appendix) between March 14 and 31 to Eq. 7, the parameters of the model can be determined. 4: The number of people who are infected and carrying the virus without being identified, I(t), as a function of time, with March 14 as day 0.

abstract:
  At the time of writing, Louisiana has the third highest COVID-19 infection per capita in the United States. The state government issued a stay-at-home order effective March 23rd. We analyze the projected spread of COVID-19 in Louisiana without including the effects of the stay-at-home order. We predict that a large fraction of the state population would be infected without the mitigation efforts, and would certainly overwhelm the capacity of Louisiana health care system. We further predict the outcomes with different degrees of reduction in the infection rate. More than 70% of reduction is required to cap the number of infected to under one million.


id: cord-151198-4fjya9wn
title: Ending the COVID-19 epidemic in the United Kingdom
author: Rogers, L C G
date: 2020-04-26
txt: ./txt/cord-151198-4fjya9wn.txt
cache: ./cache/cord-151198-4fjya9wn.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2004.12462v1.pdf
doi: nan
summary:
  Social distancing and lockdown are the two main non-pharmaceutical interventions being used by the UK government to contain and control the COVID-19 epidemic; these are being applied uniformly across the entire country, even though the results of the Imperial College report by Ferguson et al show that the impact of the infection increases sharply with age. We will denote by N j (t) the total number of j-individuals in the population at time t, and allow this to change gradually with the influx of new births, visitors from other countries; this is to model the possibility that new infecteds come in from outside and reignite the epidemic. where ι j and σ j are known functions of time representing the arrival of new asymptomatic infec-1 https://colab.research.google.com/drive/1tbB47uSGIA0WehY-hvIYgdO0mpnZU5A8 tives and susceptibles respectively 2 ; and the final term on the right-hand side of (3) allows for the possibility that removed infectives may not in fact be immune, and some may return to the population ready for reinfection.

abstract:
  Social distancing and lockdown are the two main non-pharmaceutical interventions being used by the UK government to contain and control the COVID-19 epidemic; these are being applied uniformly across the entire country, even though the results of the Imperial College report by Ferguson et al show that the impact of the infection increases sharply with age. This paper develops a variant of the workhorse SIR model for epidemics, where the population is classified into a number of age groups. This allows us to understand the effects of age-dependent controls on the epidemic, and explore possible exit strategies.


id: cord-158219-hk55bzqm
author: Cintia, Paolo
date: 2020-06-04
url: https://arxiv.org/pdf/2006.03141v1.pdf
doi: nan
title:
  The relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy

abstract:
  We describe in this report our studies to understand the relationship between human mobility and the spreading of COVID-19, as an aid to manage the restart of the social and economic activities after the lockdown and monitor the epidemics in the coming weeks and months. We compare the evolution (from January to May 2020) of the daily mobility flows in Italy, measured by means of nation-wide mobile phone data, and the evolution of transmissibility, measured by the net reproduction number, i.e., the mean number of secondary infections generated by one primary infector in the presence of control interventions and human behavioural adaptations. We find a striking relationship between the negative variation of mobility flows and the net reproduction number, in all Italian regions, between March 11th and March 18th, when the country entered the lockdown. This observation allows us to quantify the time needed to"switch off"the country mobility (one week) and the time required to bring the net reproduction number below 1 (one week). A reasonably simple regression model provides evidence that the net reproduction number is correlated with a region's incoming, outgoing and internal mobility. We also find a strong relationship between the number of days above the epidemic threshold before the mobility flows reduce significantly as an effect of lockdowns, and the total number of confirmed SARS-CoV-2 infections per 100k inhabitants, thus indirectly showing the effectiveness of the lockdown and the other non-pharmaceutical interventions in the containment of the contagion. Our study demonstrates the value of"big"mobility data to the monitoring of key epidemic indicators to inform choices as the epidemics unfolds in the coming months.


id: cord-181220-gr29zq1o
title: A Study on The Effectiveness of Lock-down Measures to Control The Spread of COVID-19
author: Ghosh, Subhas Kumar
date: 2020-08-09
txt: ./txt/cord-181220-gr29zq1o.txt
cache: ./cache/cord-181220-gr29zq1o.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2008.05876v1.pdf
doi: nan
summary:
  In order to estimate the counterfactual metric (say number of deaths), we use a geographic location as a treatment unit (say Italy) and a set of other geographic locations as donor group (say Brazil and United States). In this section we present three examples of the application of m-RSC to derive the counterfactual estimates of possible number of deaths under the changed conditions like delaying or starting the stringency measures at earlier date. In this work we use Multi-dimensional Robust Synthetic Control to understand the effects of stringency measure on COVID-19 pandemic. We construct synthetic version of a location using convex combination of other geographic locations in the donor pool that most closely resembled the treatment unit in terms of pre-intervention period using stringency index and adherence score (using mobility information).

abstract:
  The ongoing pandemic of coronavirus disease 2019-2020 (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). This pathogenic virus is able to spread asymptotically during its incubation stage through a vulnerable population. Given the state of healthcare, policymakers were urged to contain the spread of infection, minimize stress on the health systems and ensure public safety. Most effective tool that was at their disposal was to close non-essential business and issue a stay home order. In this paper we consider techniques to measure the effectiveness of stringency measures adopted by governments across the world. Analyzing effectiveness of control measures like lock-down allows us to understand whether the decisions made were optimal and resulted in a reduction of burden on the healthcare system. In specific we consider using a synthetic control to construct alternative scenarios and understand what would have been the effect on health if less stringent measures were adopted. We present analysis for The State of New York, United States, Italy and The Indian capital city Delhi and show how lock-down measures has helped and what the counterfactual scenarios would have been in comparison to the current state of affairs. We show that in The State of New York the number of deaths could have been 6 times higher, and in Italy, the number of deaths could have been 3 times higher by 26th of June, 2020.


id: cord-216208-kn0njkqg
title: A simple iterative map forecast of the COVID-19 pandemic
author: Botha, Andr''e E.
date: 2020-03-23
txt: ./txt/cord-216208-kn0njkqg.txt
cache: ./cache/cord-216208-kn0njkqg.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2003.10532v3.pdf
doi: nan
summary:
  We develop a simple 3-dimensional iterative map model to forecast the global spread of the coronavirus disease. Our model contains at most two fitting parameters, which we determine from the data supplied by the world health organisation for the total number of cases and new cases each day. The fact that the available data for the pandemic can be fitted well by a simple model such as ours suggests that past and current interventions to curb the spread of the disease, globally, may not be very effective. In developing countries such as South Africa there is also a relatively large percentage of people with compromised immunity, due to the high prevalence of human immunodeficiency virus (HIV), and this also could result in the coronavirus having a much larger impact than our model of the current global data shows.

abstract:
  We develop a simple 3-dimensional iterative map model to forecast the global spread of the coronavirus disease. Our model contains at most two fitting parameters, which we determine from the data supplied by the world health organisation for the total number of cases and new cases each day. We find that our model provides a surprisingly good fit to the currently-available data, which exhibits a cross-over from exponential to power-law growth, as lock-down measures begin to take effect. Before these measures, our model predicts exponential growth from day 30 to 69, starting from the date on which the world health organisation provided the first `Situation report' (21 January 2020 $-$ day 1). Based on this initial data the disease may be expected to infect approximately 23% of the global population, i.e. about 1.76 billion people, taking approximately 83 million lives. Under this scenario, the global number of new cases is predicted to peak on day 133 (about the middle of May 2020), with an estimated 60 million new cases per day. If current lock-down measures can be maintained, our model predicts power law growth from day 69 onward. Such growth is comparatively slow and would have to continue for several decades before a sufficient number of people (at least 23% of the global population) have developed immunity to the disease through being infected. Lock-down measures appear to be very effective in postponing the unimaginably large peak in the daily number of new cases that would occur in the absence of any interventions. However, should these measure be relaxed, the spread of the disease will most likely revert back to its original exponential growth pattern. As such, the duration and severity of the lock-down measures should be carefully timed against their potentially devastating impact on the world economy.


id: cord-221131-44n5pojb
title: Some numerical observations about the COVID-19 epidemic in Italy
author: Zullo, Federico
date: 2020-03-25
txt: ./txt/cord-221131-44n5pojb.txt
cache: ./cache/cord-221131-44n5pojb.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2003.11363v2.pdf
doi: nan
summary:
  Since the start of the epidemic in China, a certain number of studies appeared in the mathematical community about this subject: the description of the spatial or temporal diffusion of the infected in given regions [4] , [8] [10] , the transmission dynamics of the infection [6] , the economic and financial consequences of the epidemic [1] , the effect of atmospheric indicators on the spread of the virus [5] , are only a fraction of the topics under investigation in these days. The reasonable assumption that the same fraction (with respect to the total) of infected, susceptible and recovered individuals are known, gives the possibility, in this case, to compare the measured data with the properties that are scale-independent. The second hypothesis is fundamental since we are going to look at scale-independent quantities: even in the case the measured number of infected and recovered individuals are different from the actual values, it is possible to estimate these quantities.

abstract:
  We give some numerical observations on the total number of infected by the SARS-CoV-2 in Italy. The analysis is based on a tanh formula involving two parameters. A polynomial correlation between the parameters gives an upper bound for the time of the peak of new infected. A numerical indicator of the temporal variability of the upper bound is introduced. The result and the possibility to extend the analysis to other countries are discussed in the conclusions.


id: cord-223212-5j5r6dd5
title: Estimates of the proportion of SARS-CoV-2 infected individuals in Sweden
author: Hult, Henrik
date: 2020-05-25
url: https://arxiv.org/pdf/2005.13519v1.pdf
doi: nan
abstract:
  In this paper a Bayesian SEIR model is studied to estimate the proportion of the population infected with SARS-CoV-2, the virus responsible for COVID-19. To capture heterogeneity in the population and the effect of interventions to reduce the rate of epidemic spread, the model uses a time-varying contact rate, whose logarithm has a Gaussian process prior. A Poisson point process is used to model the occurrence of deaths due to COVID-19 and the model is calibrated using data of daily death counts in combination with a snapshot of the the proportion of individuals with an active infection, performed in Stockholm in late March. The methodology is applied to regions in Sweden. The results show that the estimated proportion of the population who has been infected is around 13.5% in Stockholm, by 2020-05-15, and ranges between 2.5% - 15.6% in the other investigated regions. In Stockholm where the peak of daily death counts is likely behind us, parameter uncertainty does not heavily influence the expected daily number of deaths, nor the expected cumulative number of deaths. It does, however, impact the estimated cumulative number of infected individuals. In the other regions, where random sampling of the number of active infections is not available, parameter sharing is used to improve estimates, but the parameter uncertainty remains substantial.


id: cord-238241-ncz1b8dl
title: Infections and Identified Cases of COVID-19 from Random Testing Data
author: Caldwell, Allen
date: 2020-05-19
url: https://arxiv.org/pdf/2005.11277v1.pdf
doi: nan
abstract:
  There are many hard-to-reconcile numbers circulating concerning Covid-19. Using reports from random testing, the fatality ratio per infection is evaluated and used to extract further information on the actual fraction of infections and the success of their identification for different countries.


id: cord-248301-hddxaatp
author: Howard, Daniel
date: 2020-06-17
txt: ./txt/cord-248301-hddxaatp.txt
cache: ./cache/cord-248301-hddxaatp.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2006.10748v1.pdf
doi: nan
summary:
  A number of alternatives for this computation are presented and results of numerical experiments involving over 230 people of various ages and background health levels in over 1700 visits that take place over three consecutive days. A novel partial infection model is introduced to discuss these proof of concept solutions which are compared to round robin uninformed time scheduling for visits to places. A method of optimization, in this proof of concept this is a Genetic Programming [7] method, takes these requests and simulates the outings by means of an infection model, to discover a nearly optimal allocation of precise time slots for visits that reduce the likely hospitalization and death numbers.

title:
  Genetic Programming visitation scheduling solution can deliver a less austere COVID-19 pandemic population lockdown

abstract:
  A computational methodology is introduced to minimize infection opportunities for people suffering some degree of lockdown in response to a pandemic, as is the 2020 COVID-19 pandemic. Persons use their mobile phone or computational device to request trips to places of their need or interest indicating a rough time of day: `morning', `afternoon', `night' or `any time' when they would like to undertake these outings as well as the desired place to visit. An artificial intelligence methodology which is a variant of Genetic Programming studies all requests and responds with specific time allocations for such visits that minimize the overall risks of infection, hospitalization and death of people. A number of alternatives for this computation are presented and results of numerical experiments involving over 230 people of various ages and background health levels in over 1700 visits that take place over three consecutive days. A novel partial infection model is introduced to discuss these proof of concept solutions which are compared to round robin uninformed time scheduling for visits to places. The computations indicate vast improvements with far fewer dead and hospitalized. These auger well for a more realistic study using accurate infection models with the view to test deployment in the real world. The input that drives the infection model is the degree of infection by taxonomic class, such as the information that may arise from population testing for COVID-19 or, alternatively, any contamination model. The taxonomy class assumed in the computations is the likely level of infection by age group.


id: cord-252556-o4fyjqss
title: Chaos, Percolation and the Coronavirus Spread: a two-step model.
author: Bonasera, A.
date: 2020-05-11
txt: ./txt/cord-252556-o4fyjqss.txt
cache: ./cache/cord-252556-o4fyjqss.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.05.07.20094235
doi: 10.1101/2020.05.07.20094235
summary:
  The model has successfully predicted the rise and saturation of the spreading in terms of probabilities, i.e. the number of infected (or deceased) persons divided by the total number of tests performed. Among the EU countries, Germany shows the lowest number of deceased cases, which could be due to different ways of counting (for instance performing autopsies to check for the virus like in Italy). Thus in order to better stress the efficacy of the quarantine, we have plotted in figure 2 the number of cases DIVIDED by the population density, assuming that it is much easier to perform social distancing if the population density is low. Equation (1) has the same form observed in the figures (1) and (2) , but in reality it should be applied not the number of positives (or deceased) but to their probabilities, i.e. the number of cases divided by the total number of tests.

abstract:
  We discuss a two-step model for the rise and decay of the COVID-19. The first stage is well described by the same equation for turbulent flows and chaotic maps: a small number of infected d0 grows exponentially to a saturation value d{infty}. The typical growth time is given by {tau}=1/{lambda}, where {lambda} is the Lyapunov exponent. After a time tcrit determined by social distancing and/or other measures, the spread decreases exponentially as for nuclear decays and non-chaotic maps. A few countries, like China, S. Korea, Italy are in this second stage while other including the USA is near the end of the growth stage. The model predicts 15,000 ({+/-}1,500) casualties for the Lombardy region (Italy) at the end of the spreading around May 10,2020. Without the quarantine, the casualties would have been more than 50,000, hundred days after the start of the epidemics. The data from the 50 US states are of very poor quality because of an extremely late response to the epidemics, resulting unfortunately in a large number of casualties, more than 70,000 on May 6,2020. S. Korea, notwithstanding the high population density (511/km{superscript 2}) and the closeness to China, responded best to the epidemics with 255 deceased as of May 6,2020.


id: cord-257274-fzyamd7v
title: How the COVID-19 pandemic is affecting paediatric orthopaedics practice: a preliminary report
author: Peiro-Garcia, Alejandro
date: 2020-06-01
txt: ./txt/cord-257274-fzyamd7v.txt
cache: ./cache/cord-257274-fzyamd7v.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32582381/
doi: 10.1302/1863-2548.14.200099
summary:
  CONCLUSION: According to our results, the pandemic has significantly affected our daily practice by decreasing elective surgeries and onsite clinics, but other activities have increased. Census data from 14 March 2018 to 14 April 2020, including our paediatric orthopaedics outpatient clinic, paediatric trauma emergency department (ED) and paediatric orthopaedic and trauma surgical cases were reviewed to compare the effects of the COVID-19 outbreak. In Figure 2 , *Univariate statistical analysis consisted of a student two-tailed t-test to compare the outcomes of mean number of consultations (including onsite and telemedicine), mean number of surgical procedures (including elective and urgent) and emergencies between 2018, 2019 and 2020 (including triage level). As the COVID-19 pandemic has interfered in our daily practice, we have found a decrease in the number of paediatric trauma patients admitted to our ED, the number of patients visiting onsite to our paediatric orthopaedic clinic and the number of elective cases compared with other years.

abstract:
  PURPOSE: Since the state of alarm was decreed in Spain on 14 March 2020, the coronavirus disease 2019 (COVID-19) pandemic has had an extraordinary impact in paediatric hospitals. This study shows the effect of the pandemic on our practice in paediatric orthopaedics in a referral third level paediatric hospital. METHODS: We performed a single-centre retrospective review of the official census from a third level paediatric hospital from 14 March to 14 April for the years 2018, 2019 and 2020. RESULTS: The patients seen in our clinic during this period in 2020 decreased in by 82% (p < 0.001) compared with 2018 and 2019, however, the number of telemedicine consultations increased by 90.21% (p < 0.001). The total number of patients attending the clinic (including onsite and virtual) was reduced by 54.25% (p < 0.001). The total surgeries performed plummeted by 81% in this period in 2020 (p < 0.001) due to a reduction in elective cases of 94.6% (p < 0.001). No significant decrease was found in the number of urgent surgical cases per day in 2020 (p = 0.34). Finally, the number of orthopaedic patients admitted to our emergency department dropped by 78.6% during the state of alarm (p < 0.001). CONCLUSION: According to our results, the pandemic has significantly affected our daily practice by decreasing elective surgeries and onsite clinics, but other activities have increased. As we have implemented telemedicine and new technologies to adapt to this setback, we should take advantage of the situation to change our practice in the future to better allocate our health resources and to anticipate outbreaks. Published without peer review. LEVEL OF EVIDENCE: IV


id: cord-258102-7q854ppl
title: LOCKDOWN AS A PANDEMIC MITIGATING POLICY INTERVENTION IN INDIA
author: Mandal, S.
date: 2020-06-20
txt: ./txt/cord-258102-7q854ppl.txt
cache: ./cache/cord-258102-7q854ppl.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.06.19.20134437
doi: 10.1101/2020.06.19.20134437
summary:
  We use publicly available timeline data on the Covid-19 outbreak for nine indian states to calculate the important quantifier of the outbreak, the sought after Rt or the time varying reproduction number of the outbreak. This number can faithfully tell us the success of lockdown measures inside indian states, as containment policy for the spread of Covid-19 viral disease. The instantaneous version of basic reproduction number [14] of the infection is plotted against time to gauge the success [15] (or lack thereof) [16] of this policy intervention in nine dierent states of India. We set S (0) equals the population of the region, R(0) = 0, I (0) is 10 to 14 times the average number of conrmed cases from Day 0 to Day 7, and γ the inverse of mean infectious period, obtained from the parametrization of serial interval distribution collected directly from data described in section (3) .

abstract:
  Abstract. We use publicly available timeline data on the Covid-19 outbreak for nine indian states to calculate the important quantifier of the outbreak, the sought after Rt or the time varying reproduction number of the outbreak. This quantity can be measured in in several ways, e.g. by application of Stochastic compartmentalised SIR (DCM) model, Poissonian likelihood based (ML) model & the exponential growth rate (EGR) model. The third one is known as the effective reproduction number of an outbreak. Here we use, mostly, the second one. It is known as the instantaneous reproduction number for an outbreak. This number can faithfully tell us the success of lockdown measures inside indian states, as containment policy for the spread of Covid-19 viral disease. This can also, indirectly yield notional value of the generation time inteval in different states. In doing this work we employ, pan India serial interval of the outbreak estimated directly from data from January 30th to April 19th, 2020. Simultaneously, in conjunction with the serial interval data, our result is derived from incidences data between March 14th, 2020 to June 1st, 2020, for the said states. We find the lockdown had marked positive effect on the nature of time dependent reproduction number in most of the Indian states, barring a couple. The possible reason for such failures have been investigated.


id: cord-261530-vmsq5hhz
author: Rodriguez, Jorge
date: 2020-04-07
txt: ./txt/cord-261530-vmsq5hhz.txt
cache: ./cache/cord-261530-vmsq5hhz.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.04.04.20053017
doi: 10.1101/2020.04.04.20053017
summary:
  Key findings in our results indicate that (i) universal social isolation measures appear effective in reducing total fatalities only if they are strict and the number of daily social interactions is reduced to very low numbers; (ii) selective isolation of only the elderly (at higher fatality risk) appears almost as effective in reducing total fatalities but at a much lower economic damage; (iii) an increase in the number of critical care beds could save up to eight lives per extra bed in a million population with the current parameters used; (iv) the use of protective equipment (PPE) appears effective to dramatically reduce total fatalities when implemented extensively and in a high degree; (v) infection recognition through random testing of the population, accompanied by subsequent (self) isolation of infected aware individuals, can dramatically reduce the total fatalities but only if conducted extensively to almost the entire population and sustained over time; (vi) ending isolation measures while R0 values remain above 1.0 (with a safety factor) renders the isolation measures useless and total fatality numbers return to values as if nothing was ever done; (vii) ending the isolation measures for only the population under 60 y/o at R0 values still above 1.0 increases total fatalities but only around half as much as if isolation ends for everyone; (viii) a threshold value, equivalent to that for R0, appears to exist for the daily fatality rate at which to end isolation measures, this is significant as the fatality rate is (unlike R0) very accurately known.

title:
  A mechanistic population balance model to evaluate the impact of interventions on infectious disease outbreaks: Case for COVID19

abstract:
  Infectious diseases, especially when new and highly contagious, could be devastating producing epidemic outbreaks and pandemics. Predicting the outcomes of such events in relation to possible interventions is crucial for societal and healthcare planning and forecasting of resource needs. Deterministic and mechanistic models can capture the main known phenomena of epidemics while also allowing for a meaningful interpretation of results. In this work a deterministic mechanistic population balance model was developed. The model describes individuals in a population by infection stage and age group. The population is treated as in a close well mixed community with no migrations. Infection rates and clinical and epidemiological information govern the transitions between stages of the disease. The present model provides a steppingstone to build upon and its current low complexity retains accessibility to non experts and policy makers to comprehend the variables and phenomena at play. The impact of specific interventions on the outbreak time course, number of cases and outcome of fatalities were evaluated including that of available critical care. Data available from the COVID19 outbreak as of early April 2020 was used. Key findings in our results indicate that (i) universal social isolation measures appear effective in reducing total fatalities only if they are strict and the number of daily social interactions is reduced to very low numbers; (ii) selective isolation of only the elderly (at higher fatality risk) appears almost as effective in reducing total fatalities but at a much lower economic damage; (iii) an increase in the number of critical care beds could save up to eight lives per extra bed in a million population with the current parameters used; (iv) the use of protective equipment (PPE) appears effective to dramatically reduce total fatalities when implemented extensively and in a high degree; (v) infection recognition through random testing of the population, accompanied by subsequent (self) isolation of infected aware individuals, can dramatically reduce the total fatalities but only if conducted extensively to almost the entire population and sustained over time; (vi) ending isolation measures while R0 values remain above 1.0 (with a safety factor) renders the isolation measures useless and total fatality numbers return to values as if nothing was ever done; (vii) ending the isolation measures for only the population under 60 y/o at R0 values still above 1.0 increases total fatalities but only around half as much as if isolation ends for everyone; (viii) a threshold value, equivalent to that for R0, appears to exist for the daily fatality rate at which to end isolation measures, this is significant as the fatality rate is (unlike R0) very accurately known. Any interpretation of these results for the COVID19 outbreak predictions and interventions should be considered only qualitatively at this stage due to the low confidence (lack of complete and valid data) on the parameter values available at the time of writing. Any quantitative interpretation of the results must be accompanied with a critical discussion in terms of the model limitations and its frame of application.


id: cord-270953-z2zwdxrk
author: Hittner, J. B.
date: 2020-05-16
txt: ./txt/cord-270953-z2zwdxrk.txt
cache: ./cache/cord-270953-z2zwdxrk.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.05.14.20102483
doi: 10.1101/2020.05.14.20102483
summary:
  Analyzing the epidemic data reported in all 50 states of the USA, 61 during March of 2020 (the month when testing started), we investigated whether testing-related 62 variables -including massive and early testing− predict mortality. However, for predicting 86 deaths per million citizens, the apparent prevalence rate was a 3.5 times stronger predictor than 87 was the number of confirmed cases (Supplemental Table 2B) . Whether cases or fatalities are considered, findings indicate that reporting COVID-19 93 data as counts is not as informative as reporting metrics that consider two or more interacting 94 quantities, such as the apparent prevalence rate and the number of deaths/million citizens. For example, a recombination of those variables (the number of tests 105 performed in week I/million citizens/population density) empirically demonstrate that massive 106 and early testing may save lives (Figs.

title:
  Early and massive testing saves lives: COVID-19 related infections and deaths in the United States during March of 2020

abstract:
  To optimize epidemiologic interventions, predictors of mortality should be identified. The US COVID-19 epidemic data, reported up to 31 March 2020, were analyzed using kernel regularized least squares regression. Six potential predictors of mortality were investigated: (i) the number of diagnostic tests performed in testing week I; (ii) the proportion of all tests conducted during week I of testing; (iii) the cumulative number of (test-positive) cases through 3-31-2020, (iv) the number of tests performed/million citizens; (v) the cumulative number of citizens tested; and (vi) the apparent prevalence rate, defined as the number of cases/million citizens. Two metrics estimated mortality: the number of deaths and the number of deaths/million citizens. While both expressions of mortality were predicted by the case count and the apparent prevalence rate, the number of deaths/million citizens was {approx}3.5 times better predicted by the apparent prevalence rate than the number of cases. In eighteen states, early testing/million citizens/population density was inversely associated with the cumulative mortality reported by 31 March, 2020. Findings support the hypothesis that early and massive testing saves lives. Other factors --e.g., population density-- may also influence outcomes. To optimize national and local policies, the creation and dissemination of high resolution geo-referenced, epidemic data is recommended.


id: cord-272085-4mqc8mqd
title: Impact of Lockdown on the Epidemic Dynamics of COVID-19 in France
author: Roques, Lionel
date: 2020-06-05
txt: ./txt/cord-272085-4mqc8mqd.txt
cache: ./cache/cord-272085-4mqc8mqd.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32582739/
doi: 10.3389/fmed.2020.00274
summary:
  Here, we develop a new mechanistic-statistical approach, based on a SIRD model (D being the dead cases compartment), in the aim of • estimating the effect of the lockdown in France on the contact rate and the effective reproduction number R e ; The computation of the solution of (1) with the posterior distribution of the parameters leads to a number of infectious I(t f ) = 7.0 · 10 5 and a total number of infected cases (including recovered) (I + R)(t f ) = 2.0 · 10 6 at the end of the observation period (April 14). We obtained an effective reproduction number that was divided by a factor 7, compared to the estimate of the R 0 carried out in France at the early stage of the epidemic, before the country went into lockdown [a value R 0 = 3.2 was obtained in (15) ].

abstract:
  The COVID-19 epidemic was reported in the Hubei province in China in December 2019 and then spread around the world reaching the pandemic stage at the beginning of March 2020. Since then, several countries went into lockdown. Using a mechanistic-statistical formalism, we estimate the effect of the lockdown in France on the contact rate and the effective reproduction number R(e) of the COVID-19. We obtain a reduction by a factor 7 (R(e) = 0.47, 95%-CI: 0.45–0.50), compared to the estimates carried out in France at the early stage of the epidemic. We also estimate the fraction of the population that would be infected by the beginning of May, at the official date at which the lockdown should be relaxed. We find a fraction of 3.7% (95%-CI: 3.0–4.8%) of the total French population, without taking into account the number of recovered individuals before April 1st, which is not known. This proportion is seemingly too low to reach herd immunity. Thus, even if the lockdown strongly mitigated the first epidemic wave, keeping a low value of R(e) is crucial to avoid an uncontrolled second wave (initiated with much more infectious cases than the first wave) and to hence avoid the saturation of hospital facilities.


id: cord-272838-wjapj65w
author: Liou, Je-Liang
date: 2019-12-09
txt: ./txt/cord-272838-wjapj65w.txt
cache: ./cache/cord-272838-wjapj65w.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.tourman.2019.104055
doi: 10.1016/j.tourman.2019.104055
summary:
  This study employs an extended gravity model to analyse the complementarity or competitiveness relationship of the number of inbound tourists and corresponding tourism revenue between China and 19 other nations under the implementation of China''s Open-door Tourism Policy to Taiwan in 2008. Other studies have indicated that factors such as the security of the travelling spot, gourmet food, and scenic views are crucial for tourism decisions (Cîrstea, 2014; Enright & Table 1 Total number of tourists from the major nations to Taiwan, 2001 Taiwan, -2017 Year The other four inbound nations are India, Thailand, the Philippines, and Vietnam. The purpose of this study is to employ an extended gravity model (EGM) to explore the relationship between the change in the number of inbound tourists and the corresponding tourism revenue from China and from visitors from 19 other major nations to Taiwan in 2001-2017 under China''s Open-door Policy to Taiwan.

title:
  The effect of China''s open-door tourism policy on Taiwan: Promoting or suppressing tourism from other countries to Taiwan?

abstract:
  This study employs an extended gravity model to analyse the complementarity or competitiveness relationship of the number of inbound tourists and corresponding tourism revenue between China and 19 other nations under the implementation of China's Open-door Tourism Policy to Taiwan in 2008. A simulation for 2018–2021 demonstrates the sustained impact of this policy. The results show that the number of tourists to Taiwan from China reached its peak in 2015 at 41% and will decrease to 9% by 2021. The corresponding tourism revenue will decrease from 49% to 11% over the same period. The results also show that if the number of tourists from China remains above 836,772, the number of tourists from Japan, Hong Kong, Australasia, North America, and Europe will still increase. However, the number of tourists from South Korea and South and Southeast Asia will increase continuously regardless of tourists from China, even far below 836,772.


id: cord-273199-xmq502gm
title: A surprising formula for the spread of Covid-19 under aggressive management
author: Cherednik, I.
date: 2020-05-02
txt: ./txt/cord-273199-xmq502gm.txt
cache: ./cache/cord-273199-xmq502gm.txt
extension: .txt
mime: text/plain
url: http://medrxiv.org/cgi/content/short/2020.04.29.20084483v1?rss=1
doi: 10.1101/2020.04.29.20084483
summary:
  We propose an algebraic-type formula that describes with high accuracy the spread of Covid-19 pandemic under aggressive management for the periods of the intensive growth of the total number of infections. Anyway a sociological approach to the spread, which "explains" under some assumptions the power growth of the number of total cases, is quite natural in our work, because the active managements of epidemics is clearly of sociological nature, applicable only to humans. . https://doi.org/10.1101/2020.04.29.20084483 doi: medRxiv preprint An important outcome of our modeling is that the measures of "hard type", like detecting and isolating infected people and closing the places where the spread is almost inevitable, are the key for ending an epidemic. The predictions are of course based on the assumption that the intensity of hard measures continues to be proportional to the total number of detected infections to date, as it was clearly the case for the red dots.

abstract:
  We propose an algebraic-type formula that describes with high accuracy the spread of Covid-19 pandemic under aggressive management for the periods of the intensive growth of the total number of infections. The formula can be used as a powerful forecasting tool. The parameters of the theory are the transmission rate, reflecting the viral fitness and "normal" frequency of contacts in the infected areas, and the intensity of prevention measures. The duration of the period of intensive growth is essentially inversely proportional to the square root of the intensity of hard measures. A more precise formula is based on Bessel functions. The data for the USA, UK, Sweden, Israel are provided.


id: cord-276870-gxtvlji7
title: Learning from history: do not flatten the curve of antiviral research!
author: Bobrowski, Tesia
date: 2020-07-15
url: https://doi.org/10.1016/j.drudis.2020.07.008
doi: 10.1016/j.drudis.2020.07.008
abstract:
  Here, we explore the dynamics of the response of the scientific community to several epidemics, including Coronavirus 2019 (COVID-19), as assessed by the numbers of clinical trials, publications, and level of research funding over time. All six prior epidemics studied [bird flu, severe acute respiratory syndrome (SARS), swine flu, Middle East Respiratory Syndrome (MERS), Ebola, and Zika] were characterized by an initial spike of research response that flattened shortly thereafter. Unfortunately, no antiviral medications have been discovered to date as treatments for any of these diseases. By contrast, the HIV/AIDS pandemic has garnered consistent research investment since it began and resulted in drugs being developed within 7 years of its start date, with many more to follow. We argue that, to develop effective treatments for COVID-19 and be prepared for future epidemics, long-term, consistent investment in antiviral research is needed.


id: cord-279245-z8pafxok
title: Chaos, Percolation and the Coronavirus Spread: the Italian case
author: Bonasera, Aldo
date: 2020-04-14
url: https://doi.org/10.1101/2020.04.10.20060616
doi: 10.1101/2020.04.10.20060616
abstract:
  A model based on chaotic maps and turbulent flows is applied to the spread of Coronavirus for each Italian region in order to obtain useful information and help to contrast it. We divide the regions into different risk categories and discuss anomalies. The worst cases are confined between the Appenine and the Alps mountain ranges but the situation seem to improve closer to the sea. The Veneto region gave the most efficient response so far and some of their resources could be diverted to other regions, in particular more tests to the Lombardia, Liguria, Piemonte, Marche and V. Aosta regions, which seem to be worst affected. We noticed worrying anomalies in the Lazio, Campania and Sicilia regions to be monitored. We stress that the number of fatalities we predicted on March 12 has been confirmed daily by the bulletins. This suggests a change of strategy in order to reduce such number maybe moving the weaker population (and negative to the virus test) to beach resorts, which should be empty presently. The ratio deceased/positives on April 4, 2020 is 5.4% worldwide, 12.3% in Italy, 1.4% in Germany, 2.7% in the USA, 10.3% in the UK and 4.1% in China. These large fluctuations should be investigated starting from the Italian regions, which show similar large fluctuations.


id: cord-282849-ve8krq78
author: Stebler, Rosa
date: 2019-09-20
url: https://doi.org/10.3389/fvets.2019.00318
doi: 10.3389/fvets.2019.00318
title:
  Extrapolating Antibiotic Sales to Number of Treated Animals: Treatments in Pigs and Calves in Switzerland, 2011–2015

abstract:
  To evaluate the contribution of antimicrobial use in human and veterinary medicine to the emergence and spread of resistant bacteria, the use of these substances has to be accurately monitored in each setting. Currently, various initiatives collect sales data of veterinary antimicrobials, thereby providing an overview of quantities on the market. However, sales data collected at the level of wholesalers or marketing authorization holders are of limited use to associate with the prevalence of bacterial resistances at species level. We converted sales data to the number of potential treatments of calves and pigs in Switzerland for the years 2011 to 2015 using animal course doses (ACD). For each authorized product, the number of potential therapies was derived from the sales at wholesaler's level and the ACD in mg per kg. For products registered for use in multiple species, a percentage of the sales was attributed to each authorized species according to their biomass distribution. We estimated a total of 5,914,349 therapies for pigs and 1,407,450 for calves in 2015. Using the number of slaughtered animals for that year as denominator, we calculated a treatment intensity of 2.15 therapies per pig and 5.96 per calf. Between 2011 and 2015, sales of veterinary antimicrobials decreased by 30%. The calculated number of potential therapies decreased by 30% for pigs and 15% for calves. An analysis of treatment intensity at antimicrobial class level showed a decrease of 64% for colistin used in pigs, and of 7% for macrolides used in both pigs and calves. Whereas the use of 3rd and 4th generation cephalosporins in calves decreased by 15.8%, usage of fluoroquinolones increased by 10.8% in the same period. Corresponding values for pigs were −16.4 and +0.7%. This is the first extrapolation of antimicrobial usage at product level for pigs and calves in Switzerland. It shows that calves were more frequently treated than pigs with a decreasing trend for both number of therapies and use of colistin, macrolides and cephalosporins 3rd and 4th generations. Nonetheless, we calculated an increase in the usage of fluoroquinolones. Altogether, this study's outcomes allow for trend analysis and can be used to assess the relationship between antimicrobial use and resistance at the national level.


id: cord-284195-qarz4o2z
author: Ansumali, Santosh
date: 2020-04-11
txt: ./txt/cord-284195-qarz4o2z.txt
cache: ./cache/cord-284195-qarz4o2z.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.04.07.20055772
doi: 10.1101/2020.04.07.20055772
summary:
  As such, a few weeks after these strict measures, and noting the reported success of China, governments of various provinces and countries are waiting for the new daily infections to cross over the peak. To date, other than China which continues to report nearly zero new infected cases every day for the past few weeks, all other countries are either in an exponential phase or a linear growth phase. In this work, we note by studying the COVID-19 infection data from several countries which implemented quarantine that the exponential growth phase ends, but it is followed by a linear growth phase. As much as the linear regime suggests the end of the exponential growth phase, a correlation of the daily cases with the average number of infections at the time of transition seems to suggest that the growth is only maintained in a "pause", frozen at the state where the quarantines are implemented.

title:
  A Very Flat Peak: Exponential growth phase of COVID-19 is mostly followed by a prolonged linear growth phase, not an immediate saturation

abstract:
  When actively taking measures to control an epidemic, an important indicator of success is crossing the "peak" of daily new infections. The peak is a positive sign which marks the end of the exponential phase of infection spread and a transition into a phase that is a manageable. Most countries or provinces with similar but independent growth trajectories had taken drastic measures for containing the COVID-19 pandemic and are eagerly waiting to cross the peak. However, the data after many weeks of strict measures suggests that most provinces instead enter a phase where the infections are in a linear growth. While the transition out of an exponential phase is relieving, the roughly constant number of daily new infections differ widely, range from around 50 in Singapore to around 2000 just in Lombardy (Italy), and 7600 in Spain. The daily new infection rate of a region seems to depend heavily on the time point in the exponential evolution when the restrictive measures were adopted, rather than on the population of the region. It is not easy to point the critical source of these persistent infections. We attempt to interpret this data using a simple model of newer infections mediated by asymptomatic patients, which underscores the importance of actively identifying any potential leakages in the quarantine. Given the novelty of the virus, it is hard to predict too far into the future and one needs to be observant to see if a plan B is needed as a second round of interventions. So far, the peak achieved by most countries with the first round of intervention is extremely flat.


id: cord-286076-60iwzsp6
title: The value of superstitions
author: Ng, Travis
date: 2009-12-24
url: https://api.elsevier.com/content/article/pii/S0167487009001275
doi: 10.1016/j.joep.2009.12.002
abstract:
  This paper estimates the value of superstitions by studying the auctions of vehicle license plates. We show that the value of superstitions is economically significant, which justifies their persistence in human civilization. We also document the changes of the value of superstitions across different types of plates, across different policy regimes, and across different macroeconomic environments. Interestingly, some of the changes are rather consistent with economic intuition.


id: cord-299846-yx18oyv6
title: Pandæsim: An Epidemic Spreading Stochastic Simulator
author: Amar, Patrick
date: 2020-09-18
txt: ./txt/cord-299846-yx18oyv6.txt
cache: ./cache/cord-299846-yx18oyv6.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32962157/
doi: 10.3390/biology9090299
summary:
  The stochastic discrete version of Pandæsim showed very good correlations between the simulation results and the statistics gathered from hospitals, both on day by day and on global numbers, including the effects of the lockdown. The number of people of each age slice leaving their home sub-regions is a stochastic sample (or averaged value for the deterministic continuous solver) of a percentage of the population of this sub-region. Starting from an initial state (number of contagious people in each sub-region), the simulation algorithm iterates the following process at each timestep until either the epidemic ends or the maximum duration of the simulation is reached (defaults to 720 days). When the initial number of contagious people was relatively high, for example, in the Val-de-Marne sub-region (180), the results for both solvers were nearly identical: 5207 deaths for the average of 1000 stochastic runs and 5204 deaths for a deterministic run (Figures 2 and 3) .

abstract:
  SIMPLE SUMMARY: In order to study the efficiency of countermeasures used against the Covid-19 pandemic at the scale of a country, we designed a model and developed an efficient simulation program based on a well known discrete stochastic simulation framework along with a standard, coarse grain, spatial localisation extension. Our particular approach allows us also to implement deterministic continuous resolutions of the same model. We applied it to the Covid-19 epidemic in France where lockdown countermeasures were used. With the stochastic discrete method, we found good correlations between the simulation results and the statistics gathered from hospitals. In contrast, the deterministic continuous approach lead to very different results. We proposed an explanation based on the fact that the effects of discretisation are high for small values, but low for large values. When we add stochasticity, it can explain the differences in behaviour of those two approaches. This system is one more tool to study different countermeasures to epidemics, from lockdowns to social distancing, and also the effects of mass vaccination. It could be improved by including the possibility of individual reinfection. ABSTRACT: Many methods have been used to model epidemic spreading. They include ordinary differential equation systems for globally homogeneous environments and partial differential equation systems to take into account spatial localisation and inhomogeneity. Stochastic differential equations systems have been used to model the inherent stochasticity of epidemic spreading processes. In our case study, we wanted to model the numbers of individuals in different states of the disease, and their locations in the country. Among the many existing methods we used our own variant of the well known Gillespie stochastic algorithm, along with the sub-volumes method to take into account the spatial localisation. Our algorithm allows us to easily switch from stochastic discrete simulation to continuous deterministic resolution using mean values. We applied our approaches on the study of the Covid-19 epidemic in France. The stochastic discrete version of Pandæsim showed very good correlations between the simulation results and the statistics gathered from hospitals, both on day by day and on global numbers, including the effects of the lockdown. Moreover, we have highlighted interesting differences in behaviour between the continuous and discrete methods that may arise in some particular conditions.


id: cord-300930-47a4pu27
author: Beigel, R.
date: 2020-05-24
txt: ./txt/cord-300930-47a4pu27.txt
cache: ./cache/cord-300930-47a4pu27.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.05.22.20110585
doi: 10.1101/2020.05.22.20110585
summary:
  Mathematically, the problems of identifying infected individuals ( identification ) and estimating the total number of infected individuals in a given population ( infection rate ) are related but in fact can be addressed by subtly different algorithms to reduce the number of tests needed and thereby the total cost of doing testing. However, as we will demonstrate in this brief communication, estimating the number of infected individuals can be solved by novel adaptation of methods developed in theoretical computer science aimed at approximate counting. In addition to rate estimation we provide a review and analysis of several identification algorithms that can be deployed in communities with low infection rates that achieve reasonable improvement over the standard algorithms for group testing that have been previously explored. • Estimate the rate of the infection in the population or approximately count how many people test positive in a population of a given size with as few partially pooled tests as possible. We now describe approximate counting algorithms that use pools of samples to estimate accurate infection rates.

title:
  Rate Estimation and Identification of COVID-19 Infections: Towards Rational Policy Making During Early and Late Stages of Epidemics

abstract:
  Pandemics have a profound impact on our world, causing loss of life, affecting our culture and historically shaping our genetics. The response to a pandemic requires both resilience and imagination. It has been clearly documented that obtaining an accurate estimate and trends of the actual infection rate and mortality risk are very important for policy makers and medical professionals. One cannot estimate mortality rates without an accurate assessment of the number of infected individuals in the population. This need is also aligned with identifying the infected individuals so they can be properly treated, monitored and tracked. However, accurate estimation of the infection rate, locally, geographically and nationally is important independently. These infection rate estimates can guide policy makers at both state, national or world level to achieve a better management of risk to society. The decisions facing policy makers are very different during early stages of an emerging epidemic where the infection rate is low, middle stages where the rate is rapidly climbing, and later stages where the epidemic curve has flattened to a low and relatively sustainable rate. In this paper we provide relatively efficient pooling methods to both estimate infection rates and identify infected individuals for populations with low infection rates. These estimates may provide significant cost reductions for testing in rural communities, third world countries and other situations where the cost of testing is expensive or testing is not widely available. As we prepare for the second wave of the pandemic this line of work may provide new solutions for both the biomedical community and policy makers at all levels.


id: cord-303657-o66rchhw
title: On the true numbers of COVID-19 infections: behind the available data
author: El Qadmiry, M.
date: 2020-05-28
url: http://medrxiv.org/cgi/content/short/2020.05.26.20114074v1?rss=1
doi: 10.1101/2020.05.26.20114074
abstract:
  In December-2019 China reported several cases of a novel coronavirus later called COVID-19. In this work, we will use a probabilistic method for approximating the true daily numbers of infected. Based on two distribution functions to describe the spontaneous recovered cases on the one hand and the detected cases on the other hand. The impact of the underlying variables of these functions is discussed. The detected rate is predicted to be between 5.3% and 10,8%, which means that there would be about 38 million infected until now (10-May 2020), rather than the officially declared number of 3.99 million worldwide cases.


id: cord-304820-q3de7r1p
title: Clarifying predictions for COVID-19 from testing data: the example of New-York State
author: Griette, P.
date: 2020-10-12
txt: ./txt/cord-304820-q3de7r1p.txt
cache: ./cache/cord-304820-q3de7r1p.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.10.10.20203034
doi: 10.1101/2020.10.10.20203034
summary:
  Cumulative number of reported (tested infectious) cases at time t Daily number of reported (tested infectious) cases at time t Phenomenological models for the reported cases: At the early stage of the epidemic, we assume that all the infected components of the system grow exponentially while the number of susceptible remains unchanged during a relatively short period of time t ∈ [t 1 , t 2 ]. In figure (d) we plot the cumulative number of cases coming from the model as a function of the cumulative number of tests from the data. In Figure 8 , we replace the daily number of tests n data (t) (coming from the data for New-York''s state) in the model by either 2 × n data (t), 5 × n data (t), 10 × n data (t) or 100 × n data (t). Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data

abstract:
  In this article, we use testing data as an input of a new epidemic model. We get nice a concordance between the best fit the model to the reported cases data for New-York state. We also get a good concordance of the testing dynamic and the epidemic's dynamic in the cumulative cases. Finally, we can investigate the effect of multiplying the number of tests by 2, 5, 10, and 100 to investigate the consequences on the reduction of the number of reported cases.


id: cord-306932-6vt60348
title: Estimation of SARS-CoV-2 Infection Prevalence in Santa Clara County
author: Yadlowsky, S.
date: 2020-03-27
txt: ./txt/cord-306932-6vt60348.txt
cache: ./cache/cord-306932-6vt60348.txt
extension: .txt
mime: text/plain
url: http://medrxiv.org/cgi/content/short/2020.03.24.20043067v1?rss=1
doi: 10.1101/2020.03.24.20043067
summary:
  In the absence of wide-spread testing, we provide one approach to infer prevalence based on the assumption that the fraction of true infections needing hospitalization is fixed and that all hospitalized cases of COVID-19 in Santa Clara are identified. However, even if this were true, we expect to continue to see an increase in hospitalized cases of COVID-19 in the short term due to the fact that infection of SARS-CoV-2 on March 17th can lead to hospitalizations up to 14 days later. As input parameters to our model, we need an estimate of the lag time , and the rate of growth of infections , and hospitalization rate for COVID-19 among those infected. For the rate of growth of infections , we compared two values: the first estimated from the change in hospitalizations from March 3 to March 12 in the Santa Clara data, and the second calculated from the reported 6-9 day doubling time 3 , 4 .

abstract:
  To reliably estimate the demand on regional health systems and perform public health planning, it is necessary to have a good estimate of the prevalence of infection with SARS-CoV-2 (the virus that causes COVID-19) in the population. In the absence of wide-spread testing, we provide one approach to infer prevalence based on the assumption that the fraction of true infections needing hospitalization is fixed and that all hospitalized cases of COVID-19 in Santa Clara are identified. Our goal is to estimate the prevalence of SARS-CoV-2 infections, i.e. the true number of people currently infected with the virus, divided by the total population size. Our analysis suggests that as of March 17, 2020, there are 6,500 infections (0.34% of the population) of SARS-CoV-2 in Santa Clara County. Based on adjusting the parameters of our model to be optimistic (respectively pessimistic), the number of infections would be 1,400 (resp. 26,000), corresponding to a prevalence of 0.08% (resp. 1.36%). If the shelter-in-place led to R0 < 1, we would expect the number of infections to remain about constant for the next few weeks. However, even if this were true, we expect to continue to see an increase in hospitalized cases of COVID-19 in the short term due to the fact that infection of SARS-CoV-2 on March 17th can lead to hospitalizations up to 14 days later.


id: cord-307471-zukjh1hr
title: On the benefits of flattening the curve: A perspective()
author: Feng, Zhilan
date: 2020-05-27
txt: ./txt/cord-307471-zukjh1hr.txt
cache: ./cache/cord-307471-zukjh1hr.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0025556420300729?v=s5
doi: 10.1016/j.mbs.2020.108389
summary:
  The many variations on a graphic illustrating the impact of non-pharmaceutical measures to mitigate pandemic influenza that have appeared in recent news reports about COVID-19 suggest a need to better explain the mechanism by which social distancing reduces the spread of infectious diseases. In view of the extraordinary efforts underway to identify existing medications that are active against SARS-CoV-2 and to develop new antiviral drugs, vaccines and antibody therapies, any of which may have community-level effects, we also describe how pharmaceutical interventions affect transmission.  Social distancing refers to non-pharmaceutical measures to reduce the frequency or proximity of interpersonal encounters  The impact of these measures on epidemic curves is commonly misrepresented, suggesting a lack of understanding of the underlying mechanisms  As this may affect compliance with recommendations, we describe determinants of the magnitude and timing of peak incidence and the total number of infections  We also describe possible population-level effects of pharmaceutical interventions

abstract:
  The many variations on a graphic illustrating the impact of non-pharmaceutical measures to mitigate pandemic influenza that have appeared in recent news reports about COVID-19 suggest a need to better explain the mechanism by which social distancing reduces the spread of infectious diseases. And some reports understate one benefit of reducing the frequency or proximity of interpersonal encounters, a reduction in the total number of infections. In hopes that understanding will increase compliance, we describe how social distancing a) reduces the peak incidence of infections, b) delays the occurrence of this peak, and c) reduces the total number of infections during epidemics. In view of the extraordinary efforts underway to identify existing medications that are active against SARS-CoV-2 and to develop new antiviral drugs, vaccines and antibody therapies, any of which may have community-level effects, we also describe how pharmaceutical interventions affect transmission.


id: cord-309378-sfr1x0ob
title: Early Phase of the COVID-19 Outbreak in Hungary and Post-Lockdown Scenarios
author: Röst, Gergely
date: 2020-06-30
txt: ./txt/cord-309378-sfr1x0ob.txt
cache: ./cache/cord-309378-sfr1x0ob.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.3390/v12070708
doi: 10.3390/v12070708
summary:
  COVID-19 epidemic has been suppressed in Hungary due to timely non-pharmaceutical interventions, prompting a considerable reduction in the number of contacts and transmission of the virus. We incorporate various factors, such as age-specific measures, seasonal effects, and spatial heterogeneity to project the possible peak size and disease burden of a COVID-19 epidemic wave after the current measures are relaxed. Moreover, closing schools postpones the peak of the epidemic (by about one month in case of the above setting), suggesting that children may play a significant role in transmission due to their large number of contacts, even though they give negligible contribution to the overall mortality, cf. As control measures are being successively relaxed since May 4, we established an age-structured compartmental model to investigate several post-lockdown scenarios, and projected the epidemic curves and the demand for critical care beds assuming various levels of sustained reduction in transmission.

abstract:
  COVID-19 epidemic has been suppressed in Hungary due to timely non-pharmaceutical interventions, prompting a considerable reduction in the number of contacts and transmission of the virus. This strategy was effective in preventing epidemic growth and reducing the incidence of COVID-19 to low levels. In this report, we present the first epidemiological and statistical analysis of the early phase of the COVID-19 outbreak in Hungary. Then, we establish an age-structured compartmental model to explore alternative post-lockdown scenarios. We incorporate various factors, such as age-specific measures, seasonal effects, and spatial heterogeneity to project the possible peak size and disease burden of a COVID-19 epidemic wave after the current measures are relaxed.


id: cord-310983-kwytbhe7
author: Djurović, Igor
date: 2020-06-17
url: https://api.elsevier.com/content/article/pii/S2405844020310823
doi: 10.1016/j.heliyon.2020.e04238
title:
  Epidemiological control measures and predicted number of infections for SARS-CoV-2 Pandemic: Case Study Serbia March-April 2020

abstract:
  BACKGROUND: In this paper, we are studying the response of the Serbian government and health authorities to the SARS-CoV-2 pandemic in the early stage of the local outbreak between Mar. 15(th) and Apr. 15(th), 2020 by predictive numerical models. Such a study should be helpful to access the effectiveness of measures conducted to suppress the pandemic at a local scale. METHODS: We have performed extrapolation of the number of SARS-CoV-2 infections with the first stable set of data exploiting exponential growth (linear in logarithmic scale). Based on obtained coefficients it is performed prediction of a number of cases until the end of March. After initial exponential growth, we have changed predictive model to the generalized gamma function. Obtained results are compared with the number of infections and the prediction for the remainder of the outbreak is given. FINDINGS: We have found that the daily growth rate was above 21.5% at the beginning of the period, increased slightly after the introduction of the State of Emergency and the first set of strict epidemical control measures. It took about 13 days after the first set of strict measures to smooth daily growth. It seems that early government measures had an only moderate impact to reduce growth due to the social behavior of citizens and influx of diaspora returning to Serbia from highly affected areas, i.e., the exponential growth of infected persons is kept but with a reduced slope of about 14-15%. Anyway, it is demonstrated that period required that any measure has effect is up to 15 days after introduction, firstly to exponential growth with a smaller rate and after to smooth function representing the number of infected persons below exponential growth rate. CONCLUSIONS: Obtained results are consistent with findings from other countries, i.e., initial exponential growth slows down within the presumed incubation period of 2 weeks after adopting lockdown and other non-pharmaceutical epidemiological measures. However, it is also shown that the exponential growth can continue after this period with a smaller slope. Therefore, quarantine and other social distancing measures should be adopted as soon as possible in a case of any similar outbreak since alternatives mean prolonged epidemical situation and growing costs in human life, pressure on the health system, economy, etc. For modeling the remainder of the outbreak generalized gamma function is used showing accurate results but requiring more samples and pre-processing (data filtering) concerning exponential part of the outbreak. We have estimated the number of infected persons for the remaining part of the outbreak until the end of June.


id: cord-314211-tv1nhojk
author: Eltoukhy, Abdelrahman E. E.
date: 2020-09-27
txt: ./txt/cord-314211-tv1nhojk.txt
cache: ./cache/cord-314211-tv1nhojk.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32992643/
doi: 10.3390/ijerph17197080
summary:
  The number of COVID-19 cases can be accurately predicted by considering historical data of reported cases alongside some external factors that affect the spread of the virus. [37] have proposed an AI-based algorithm for predicting COVID-19 cases using a hybrid Recurrent Neural Network (RNN) with a Long Short-Term Memory (LSTM) model. These important factors include population, median age index, public and private healthcare expenditure, air quality as a CO 2 trend, seasonality as month of data collection, number of arrivals in the country/territory, and education index. First, there is no previous study that simultaneously considers the historical data of the number of COVID-19 cases and most of the external factors that affect the spread of the virus. These external factors include population, median age index, public and private healthcare expenditure, air quality as a CO 2 trend, seasonality as month of data collection, number of arrivals in the country/territory, and education index.

title:
  Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations

abstract:
  The outbreak of the 2019 novel coronavirus disease (COVID-19) has adversely affected many countries in the world. The unexpected large number of COVID-19 cases has disrupted the healthcare system in many countries and resulted in a shortage of bed spaces in the hospitals. Consequently, predicting the number of COVID-19 cases is imperative for governments to take appropriate actions. The number of COVID-19 cases can be accurately predicted by considering historical data of reported cases alongside some external factors that affect the spread of the virus. In the literature, most of the existing prediction methods focus only on the historical data and overlook most of the external factors. Hence, the number of COVID-19 cases is inaccurately predicted. Therefore, the main objective of this study is to simultaneously consider historical data and the external factors. This can be accomplished by adopting data analytics, which include developing a nonlinear autoregressive exogenous input (NARX) neural network-based algorithm. The viability and superiority of the developed algorithm are demonstrated by conducting experiments using data collected for top five affected countries in each continent. The results show an improved accuracy when compared with existing methods. Moreover, the experiments are extended to make future prediction for the number of patients afflicted with COVID-19 during the period from August 2020 until September 2020. By using such predictions, both the government and people in the affected countries can take appropriate measures to resume pre-epidemic activities.


id: cord-317093-c70c1op4
title: Urban transportation energy and carbon dioxide emission reduction strategies()
author: Cheng, Yung-Hsiang
date: 2015-11-01
txt: ./txt/cord-317093-c70c1op4.txt
cache: ./cache/cord-317093-c70c1op4.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.apenergy.2015.01.126
doi: 10.1016/j.apenergy.2015.01.126
summary:
  With the aid of an SD model, we selected Kaohsiung as a case study to explore the effects of variations in demographics, fuel prices, and economic growth rate, among other factors, on the number of vehicles, fuel consumption, and energy-related CO 2 emissions. An SD model was used to evaluate the influence of the traditional supply chain and the vendor-managed inventory system on the performance of a firm''s supply chain [36] ; to examine the effects of policy scenarios on traffic volume, modal share, energy conservation, and CO 2 mitigation [37] ; and to investigate how incorporated systems, such as population, economy, transportation demand, transportation supply, and the vehicular emission of nitrous oxides, affect the dynamic development of urban transportation systems under five policy interventions on vehicle ownership [38] .

abstract:
  Sustainability is an urban development priority. Thus, energy and carbon dioxide emission reduction is becoming more significant in the sustainability of urban transportation systems. However, urban transportation systems are complex and involve social, economic, and environmental aspects. We present solutions for a sustainable urban transportation system by establishing a simplified system dynamics model with a timeframe of 30 years (from 1995 to 2025) to simulate the effects of urban transportation management policies and to explore their potential in reducing vehicular fuel consumption and mitigating CO(2) emissions. Kaohsiung City was selected as a case study because it is the second largest metropolis in Taiwan and is an important industrial center. Three policies are examined in the study including fuel tax, motorcycle parking management, and free bus service. Simulation results indicate that both the fuel tax and motorcycle parking management policies are suggested as potentially the most effective methods for restraining the growth of the number of private vehicles, the amount of fuel consumption, and CO(2) emissions. We also conducted a synthetic policy consisting of all policies which outperforms the three individual policies. The conclusions of this study can assist urban transport planners in designing appropriate urban transport management strategies and can assist transport operation agencies in creating operational strategies to reduce their energy consumption and CO(2) emissions. The proposed approach should be generalized in other cities to develop an appropriate model to understand the various effects of policies on energy and CO(2) emissions.


id: cord-319323-1qt7vf59
author: Chakraborty, Amartya
date: 2020-10-21
url: https://doi.org/10.1007/s42001-020-00088-3
doi: 10.1007/s42001-020-00088-3
title:
  Around the world in 60 days: an exploratory study of impact of COVID-19 on online global news sentiment

abstract:
  The world is going through an unprecedented crisis due to COVID-19 breakout, and people all over the world are forced to stay indoors for safety. In such a situation, the rise and fall of the number of affected cases or deaths has turned into a constant headline in most news channels. Consequently, there is a lack of positivity in the world-wide news published in different forms of media. Texts based on news articles, movie reviews, tweets, etc. are often analyzed by researchers, and mined for determining opinion or sentiment, using supervised and unsupervised methods. The proposed work takes up the challenge of mining a comprehensive set of online news texts, for determining the prevailing sentiment in the context of the ongoing pandemic, along with a statistical analysis of the relation between actual effect of COVID-19 and online news sentiment. The amount and observed delay of impact of the ground truth situation on online news is determined on a global scale, as well as at country level. The authors conclude that at a global level, the news sentiment has a good amount of dependence on the number of new cases or deaths, while the effect varies for different countries, and is also dependent on regional socio-political factors.


id: cord-326740-1fjr9qr4
title: Reducing Risk of Infection - the COVID-19 Queueing Game
author: Perlman, Yael
date: 2020-09-03
txt: ./txt/cord-326740-1fjr9qr4.txt
cache: ./cache/cord-326740-1fjr9qr4.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32908330/
doi: 10.1016/j.ssci.2020.104987
summary:
  We propose a novel approach by which to calculate the risk of a customer being infected while queueing outside the store, while shopping, and while checking out with a cashier. We derive equilibrium strategies for a Stackelberg game in which the authority acts as a leader who first chooses the maximum number of customers allowed inside the store to minimize the risk of infection. In the second model, we analyze reducing waiting time in the payment queue (and ensuring the safety of cashiers and customers) by allowing store management to set aside a separate waiting space with limited capacity adjacent to the cashiers. In the game, the authority chooses a maximum number of customers allowed inside the store at a time to minimize the risk of transmission. Thus, in this setting, the store is divided into two separate areas: (i) the payment area with c ≥ 1 parallel cashiers and waiting space of size N customers and (ii) the shopping area, in which the maximum number of customers allowed, K.

abstract:
  The COVID-19 pandemic has forced numerous businesses such as department stores and supermarkets to limit the number of shoppers inside the store at any given time to minimize infection rates. We construct and analyze two models designed to optimize queue sizes and customer waiting times to ensure safety. In both models, customers arrive randomly at the store and, after receiving permission to enter, pass through two service phases: shopping and payment. Each customer spends a random period of time shopping (first phase) and then proceeds to the payment area of the store (second phase) where cashiers are assigned to serve customers. We propose a novel approach by which to calculate the risk of a customer being infected while queueing outside the store, while shopping, and while checking out with a cashier. The risk is proportional to the second factorial moment of the number of customers occupying the space in each phase of the shopping route. We derive equilibrium strategies for a Stackelberg game in which the authority acts as a leader who first chooses the maximum number of customers allowed inside the store to minimize the risk of infection. In the first model, store’ management chooses the number of cashiers to provide to minimize its operational costs and its customers’ implied waiting costs based on the number allowed in the store. In the second model, the store partitions its total space into two separate areas – one for shoppers and one for the cashiers and payers – to increase cashiers’ safety. Our findings and analysis are useful and applicable for authorities and businesses alike in their efforts to protect both customers and employees while reducing associated costs.


id: cord-326785-le2t1l8g
title: Pathological Society of Great Britain and Ireland. 163rd meeting, 3–5 July 1991
author: nan
date: 2005-06-15
txt: ./txt/cord-326785-le2t1l8g.txt
cache: ./cache/cord-326785-le2t1l8g.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/1681042/
doi: 10.1002/path.1711640412
abstract: nan
summary:
  The lesions (usually multlpleand each 5 mm orless m diameter) were identified in lung parenchymaat a distance from the tumour and consisted of thickened alveolar walls lined by prominent, distinctly atypical cells morphologically Slmllar to type I 1 pneumacytes and cytologically different to the associated turnour Reactive changes 8" lung involved by obstrmtive pneumonitis were not included !n thts Sews All of the associated tumwra were peripheral adenocarcinamas and all showed a pattern of alveolar wall spread at the tumour periphery Clinically 7 of the patients were female and all were smokers or ex-smokers The slgnlflcance of this lesion in the histogenesis of primary pulmonary ademcarcinoma IS.


id: cord-328859-qx7kvn0u
title: Transmission Dynamics and Control Methodology of COVID-19: a Modeling Study
author: Zhu, Hongjun
date: 2020-09-21
url: https://www.ncbi.nlm.nih.gov/pubmed/32982019/
doi: 10.1016/j.apm.2020.08.056
abstract:
  The coronavirus disease 2019 (COVID-19) has grown up to be a pandemic within a short span of time. To investigate transmission dynamics and then determine control methodology, we took epidemic in Wuhan as a study case. Unfortunately, to our best knowledge, the existing models are based on the common assumption that the total population follows a homogeneous spatial distribution, which is not the case for the prevalence occurred both in the community and in hospital due to the difference in the contact rate. To solve this problem, we propose a novel epidemic model called SEIR-HC, which is a model with two different social circles (i.e., individuals in hospital and community). Using the model alongside the exclusive optimization algorithm, the spread process of COVID-19 epidemic in Wuhan city is reproduced and then the propagation characteristics and unknown data are estimated. The basic reproduction number of COVID-19 is estimated to be 7.9, which is far higher than that of the severe acute respiratory syndrome (SARS). Furthermore, the control measures implemented in Wuhan are assessed and the control methodology of COVID-19 is discussed to provide guidance for limiting the epidemic spread.


id: cord-329357-ujh2nmh5
title: Simulations of the spread of COVID-19 and control policies in Tunisia
author: Ben Miled, S.
date: 2020-05-06
txt: ./txt/cord-329357-ujh2nmh5.txt
cache: ./cache/cord-329357-ujh2nmh5.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.05.02.20088492
doi: 10.1101/2020.05.02.20088492
summary:
  Our aims are first to evaluate Tunisian control policies for COVID-19 and secondly to understand the effect of different screening, quarantine and containment strategies and the rule of the asymptomatic patients on the spread of the virus in the Tunisian population. With this work, we show that Tunisian control policies are efficient in screening infected and asymptomatic individuals and that if containment and curfew are maintained the epidemic will be quickly contained. In this work, a mathematical epidemiological model for COVID-19 is developed to study and predict the effect of different screening, quarantine, and containment strategies on the spread of the virus in the Tunisian population. Let''s assume that CR(t) = χ1 exp(χ2t) − χ3 with χ, χ2 and χ3 three positive parameters that we estimate using log-linear regression on cases data (see figure 2 and table 2).

abstract:
  We develop and analyze in this work an epidemiological model for COVID-19 using Tunisian data. Our aims are first to evaluate Tunisian control policies for COVID-19 and secondly to understand the effect of different screening, quarantine and containment strategies and the rule of the asymptomatic patients on the spread of the virus in the Tunisian population. With this work, we show that Tunisian control policies are efficient in screening infected and asymptomatic individuals and that if containment and curfew are maintained the epidemic will be quickly contained.


id: cord-330956-692irru4
title: A control approach to the Covid-19 disease using a SEIHRD dynamical model
author: Pazos, F. A.
date: 2020-05-30
txt: ./txt/cord-330956-692irru4.txt
cache: ./cache/cord-330956-692irru4.txt
extension: .txt
mime: text/plain
url: http://medrxiv.org/cgi/content/short/2020.05.27.20115295v1?rss=1
doi: 10.1101/2020.05.27.20115295
summary:
  The recent worldwide epidemic of Covid-19 disease, for which there is no vaccine or medications to prevent or cure it, led to the adoption of public health measures by governments and populations in most of the affected countries to avoid the contagion and its spread. α and β are the probability of disease transmission in a single contact with exposed (infected) people times the average daily number of contacts per person and have units of 1/day. We propose the use of control theory to determine public nonpharmaceuticals interventions (NPIs) in order to control the evolution of the epidemic, avoiding the collapse of health care systems while minimizing harmful effects on the population and on the economy. Therefore, the control action needs to be calculated as a function of the number of infected people I (the number of exposed people E is quite unknown) in order to avoid future hospitalization requirements in the next 10.6 days at most.

abstract:
  The recent worldwide epidemic of Covid-19 disease, for which there is no vaccine or medications to prevent or cure it, led to the adoption of public health measures by governments and populations in most of the affected countries to avoid the contagion and its spread. These measures are known as nonpharmaceutical interventions (NPIs) and their implementation clearly produces social unrest as well as greatly affects the economy. Frequently, NPIs are implemented with an intensity quantified in an ad hoc manner. Control theory offers a worthwhile tool for determining the optimal intensity of the NPIs in order to avoid the collapse of the healthcare system while keeping them as low as possible, yielding in a policymakers concrete guidance. We propose here the use of a simple proportional controller that is robust to large parametric uncertainties in the model used.


id: cord-331375-tbuijeje
author: Villalobos, Carlos
date: 2020-09-25
txt: ./txt/cord-331375-tbuijeje.txt
cache: ./cache/cord-331375-tbuijeje.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/33102412/
doi: 10.3389/fpubh.2020.00489
summary:
  This paper provides an estimation of the accumulated detection rates and the accumulated number of infected individuals by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This paper provides an estimation of the accumulated detection rates and the accumulated number of infected individuals by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). By weighting the age-stratified IFRs by the country population agegroups shares in each country, it is possible to obtain countryspecific IFRs. The relevance of this study is 3-fold: Firstly, the estimation of the true number of infections includes not only confirmed cases but COVID-19 undetected cases, as well as SARS-CoV-2infected individuals without the disease, or in a pre-symptomatic stage. In order to provide reliable estimates of the number of SARS-CoV-2 infections and of the cumulative detection rates, it is necessary that governments provide real-time information about the number of COVID-19 deaths.

title:
  SARS-CoV-2 Infections in the World: An Estimation of the Infected Population and a Measure of How Higher Detection Rates Save Lives

abstract:
  This paper provides an estimation of the accumulated detection rates and the accumulated number of infected individuals by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Worldwide, on July 20, it has been estimated above 160 million individuals infected by SARS-CoV-2. Moreover, it is found that only about 1 out of 11 infected individuals are detected. In an information context in which population-based seroepidemiological studies are not frequently available, this study shows a parsimonious alternative to provide estimates of the number of SARS-CoV-2 infected individuals. By comparing our estimates with those provided by the population-based seroepidemiological ENE-COVID study in Spain, we confirm the utility of our approach. Then, using a cross-country regression, we investigated if differences in detection rates are associated with differences in the cumulative number of deaths. The hypothesis investigated in this study is that higher levels of detection of SARS-CoV-2 infections can reduce the risk exposure of the susceptible population with a relatively higher risk of death. Our results show that, on average, detecting 5 instead of 35 percent of the infections is associated with multiplying the number of deaths by a factor of about 6. Using this result, we estimated that 120 days after the pandemic outbreak, if the US would have tested with the same intensity as South Korea, about 85,000 out of their 126,000 reported deaths could have been avoided.


id: cord-334274-4jee19hx
title: How to remove the testing bias in CoV-2 statistics
author: Waelde, K.
date: 2020-10-16
txt: ./txt/cord-334274-4jee19hx.txt
cache: ./cache/cord-334274-4jee19hx.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.10.14.20212431
doi: 10.1101/2020.10.14.20212431
summary:
  Private and public decision making should not be based on time series of CoV-2-infections as the latter do not provide information about the true epidemic dynamics in a country. 3 We show that time series on the number of tests and time series on reported infections do not allow one to obtain information about the true state of an epidemic. It also studies the (lack of) informational content of time series on reported infections and time series on the number of tests, and the properties of the positive rate. Testing increases the positive rate if the number of tests undertaken due to symptoms 17 This paper is about conceptional issues related to the …nding an unbiased estimator for an unobserved time series. If we knew the number of Covid-19 cases, i.e. CoV-2 infections with severe acute respiratory symptoms (SARS), then we would know at least one part of epidemic dynamics (Ĩ symp (t) in our model).

abstract:
  BACKGROUND. Public health measures and private behaviour are based on reported numbers of SARS-CoV-2 infections. Some argue that testing influences the confirmed number of infections. OBJECTIVES/METHODS. Do time series on reported infections and the number of tests allow one to draw conclusions about actual infection numbers? A SIR model is presented where the true numbers of susceptible, infectious and removed individuals are unobserved. Testing is also modelled. RESULTS. Official confirmed infection numbers are likely to be biased and cannot be compared over time. The bias occurs because of different reasons for testing (e.g. by symptoms, representative or testing travellers). The paper illustrates the bias and works out the effect of the number of tests on the number of reported cases. The paper also shows that the positive rate (the ratio of positive tests to the total number of tests) is uninformative in the presence of non-representative testing. CONCLUSIONS. A severity index for epidemics is proposed that is comparable over time. This index is based on Covid-19 cases and can be obtained if the reason for testing is known.


id: cord-337992-g4bsul8u
title: Stochastic dynamics of an epidemic with recurrent spillovers from an endemic reservoir
author: Voinson, Marina
date: 2018-11-14
txt: ./txt/cord-337992-g4bsul8u.txt
cache: ./cache/cord-337992-g4bsul8u.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.jtbi.2018.08.017
doi: 10.1016/j.jtbi.2018.08.017
summary:
  We propose a simple continuous time stochastic Susceptible-Infected-Recovered model with a recurrent infection of an incidental host from a reservoir (e.g. humans by a zoonotic species), considering two modes of transmission, (1) animal-to-human and (2) human-to-human. The epidemiological processes are stochastic, which is particularly relevant in the case of transmission from the reservoir and more realistic because only a small number of individuals are expected to be infected at the beginning of an outbreak. In the case of emerging infectious diseases, no incidence is normally expected in the population so from a small number of infected individuals, the outbreak can be considered to spread. When the direct transmission increases the infection spreads more efficiently consuming a large number of susceptible individuals allowing few or no other excursion to reach the epidemiological threshold and producing only one outbreak when R 0 > 2.5.

abstract:
  Abstract Most emerging human infectious diseases have an animal origin. While zoonotic diseases originate from a reservoir, most theoretical studies have principally focused on single-host processes, either exclusively humans or exclusively animals, without considering the importance of animal to human transmission (i.e. spillover transmission) for understanding the dynamics of emerging infectious diseases. Here we aim to investigate the importance of spillover transmission for explaining the number and the size of outbreaks. We propose a simple continuous time stochastic Susceptible-Infected-Recovered model with a recurrent infection of an incidental host from a reservoir (e.g. humans by a zoonotic species), considering two modes of transmission, (1) animal-to-human and (2) human-to-human. The model assumes that (i) epidemiological processes are faster than other processes such as demographics or pathogen evolution and that (ii) an epidemic occurs until there are no susceptible individuals left. The results show that during an epidemic, even when the pathogens are barely contagious, multiple outbreaks are observed due to spillover transmission. Overall, the findings demonstrate that the only consideration of direct transmission between individuals is not sufficient to explain the dynamics of zoonotic pathogens in an incidental host.


id: cord-340131-refvewcm
author: Kache, Tom
date: 2020-06-04
url: https://doi.org/10.1111/apha.13520
doi: 10.1111/apha.13520
title:
  How Simulations May Help Us to Understand the Dynamics of COVID‐19 Spread. – Visualizing Non‐Intuitive Behaviors of a Pandemic (pansim.uni‐jena.de)

abstract:
  The new coronavirus SARS‐COV‐2 is currently impacting life around the globe (1). The rapid spread of this viral disease might be highly challenging for health care systems. This was seen in Northern Italy and in New York City for example(2). Governments reacted with different measures such as shutdown of all schools, universities and up to a general curfew. All of those measures have a huge impact on the economy. The United Nations secretary general has stated recently: “The COVID‐19 pandemic is one of the most dangerous challenges this world has faced in our lifetime.


id: cord-341088-bqdvx458
title: Effect of school closures on mortality from coronavirus disease 2019: old and new predictions
author: Rice, Ken
date: 2020-10-07
url: https://www.ncbi.nlm.nih.gov/pubmed/33028597/
doi: 10.1136/bmj.m3588
abstract:
  OBJECTIVE: To replicate and analyse the information available to UK policymakers when the lockdown decision was taken in March 2020 in the United Kingdom. DESIGN: Independent calculations using the CovidSim code, which implements Imperial College London’s individual based model, with data available in March 2020 applied to the coronavirus disease 2019 (covid-19) epidemic. SETTING: Simulations considering the spread of covid-19 in Great Britain and Northern Ireland. POPULATION: About 70 million simulated people matched as closely as possible to actual UK demographics, geography, and social behaviours. MAIN OUTCOME MEASURES: Replication of summary data on the covid-19 epidemic reported to the UK government Scientific Advisory Group for Emergencies (SAGE), and a detailed study of unpublished results, especially the effect of school closures. RESULTS: The CovidSim model would have produced a good forecast of the subsequent data if initialised with a reproduction number of about 3.5 for covid-19. The model predicted that school closures and isolation of younger people would increase the total number of deaths, albeit postponed to a second and subsequent waves. The findings of this study suggest that prompt interventions were shown to be highly effective at reducing peak demand for intensive care unit (ICU) beds but also prolong the epidemic, in some cases resulting in more deaths long term. This happens because covid-19 related mortality is highly skewed towards older age groups. In the absence of an effective vaccination programme, none of the proposed mitigation strategies in the UK would reduce the predicted total number of deaths below 200 000. CONCLUSIONS: It was predicted in March 2020 that in response to covid-19 a broad lockdown, as opposed to a focus on shielding the most vulnerable members of society, would reduce immediate demand for ICU beds at the cost of more deaths long term. The optimal strategy for saving lives in a covid-19 epidemic is different from that anticipated for an influenza epidemic with a different mortality age profile.


id: cord-344817-8xz7xbh1
title: The COVID-19 epidemic, its mortality, and the role of non-pharmaceutical interventions
author: Hens, Niel
date: 2020-04-30
url: https://doi.org/10.1177/2048872620924922
doi: 10.1177/2048872620924922
abstract:
  COVID-19 has developed into a pandemic, hitting hard on our communities. As the pandemic continues to bring health and economic hardship, keeping mortality as low as possible will be the highest priority for individuals; hence governments must put in place measures to ameliorate the inevitable economic downturn. The course of an epidemic may be defined by a series of key factors. In the early stages of a new infectious disease outbreak, it is crucial to understand the transmission dynamics of the infection. The basic reproduction number (R(0)), which defines the mean number of secondary cases generated by one primary case when the population is largely susceptible to infection (‘totally naïve’), determines the overall number of people who are likely to be infected, or, more precisely, the area under the epidemic curve. Estimation of changes in transmission over time can provide insights into the epidemiological situation and identify whether outbreak control measures are having a measurable effect. For R(0) > 1, the number infected tends to increase, and for R(0) < 1, transmission dies out. Non-pharmaceutical strategies to handle the epidemic are sketched and based on current knowledge, the current situation is sketched and scenarios for the near future discussed.


id: cord-344911-pw0ghz3m
author: July, Julius
date: 2020-07-22
txt: ./txt/cord-344911-pw0ghz3m.txt
cache: ./cache/cord-344911-pw0ghz3m.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/33066894/
doi: 10.1016/j.jstrokecerebrovasdis.2020.105185
summary:
  title: Impact of the coronavirus disease pandemic on the number of strokes and mechanical thrombectomies: A systematic review and meta-analysis: COVID-19 and Stroke Care BACKGROUND: This systematic review and meta-analysis aimed to evaluate the impact of the coronavirus disease (COVID-19) pandemic on stroke care, including the number of stroke alerts/codes, number of reperfusions, and number of thrombectomies during the pandemic compared to those during the pre-pandemic period. This systematic review and meta-analysis aimed to evaluate the impact of this pandemic on stroke care, including the number of stroke alerts/codes, number of reperfusions, and number of thrombectomies during the COVID-19 pandemic compared to the pre-pandemic period. Meta-analysis of proportion was used to determine the number of stroke alerts/codes, reperfusions, and mechanical thrombectomies during the pandemic compared to that during the historical pre-pandemic control period. A meta-analysis of 9 studies showed that the number of stroke alerts/codes, reperfusions, and mechanical thrombectomies was less during the pandemic period than during the prepandemic period.

title:
  Impact of the coronavirus disease pandemic on the number of strokes and mechanical thrombectomies: A systematic review and meta-analysis: COVID-19 and Stroke Care

abstract:
  BACKGROUND: This systematic review and meta-analysis aimed to evaluate the impact of the coronavirus disease (COVID-19) pandemic on stroke care, including the number of stroke alerts/codes, number of reperfusions, and number of thrombectomies during the pandemic compared to those during the pre-pandemic period. METHODS: A systematic literature search was performed using the PubMed, EuropePMC, and Cochrane Central databases. The data of interest were the number of strokes, reperfusions, and mechanical thrombectomies during the COVID-19 pandemic versus that during the pre-pandemic period (in a historical comparator group over a specified period of same period length). RESULTS: The study included 59,233 subjects from 9 studies. Meta-analysis showed that the number of stroke alerts during the pandemic was 64% (56-71%) of that during the pre-pandemic period. The number of reperfusion therapies during the pandemic was 69% (61-77%) of that during the pre-pandemic period. Pooled analysis showed that the number of mechanical thrombectomies performed during the pandemic was 78% (75-80%) of that during the pre-pandemic period. The number of mechanical thrombectomies per stroke patient was higher during the pandemic (OR 1.23 [1.12-1.36], p<0.001; I(2): 0%, p=0.845). CONCLUSION: This meta-analysis showed that the number of stroke alerts, reperfusions, and mechanical thrombectomies was reduced by 36%, 31%, and 22%, respectively, during the pandemic. However, the number of patients receiving mechanical thrombectomy per stroke increased.


id: cord-347317-qcghtkk0
author: Russo, Lucia
date: 2020-10-30
txt: ./txt/cord-347317-qcghtkk0.txt
cache: ./cache/cord-347317-qcghtkk0.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/33125393/
doi: 10.1371/journal.pone.0240649
summary:
  For the estimation of the day-zero of the outbreak in Lombardy, as well as of the "effective" per-day transmission rate for which no clinical data are available, we have used the proposed SEIIRD simulator to fit the numbers of new daily cases from February 21 to the 8th of March. Among the perplexing problems that mathematical models face when they are used to estimate epidemiological parameters and to forecast the evolution of the outbreak, two stand out: (a) the uncertainty regarding the day-zero of the outbreak, the knowledge of which is crucial to assess the stage and dynamics of the epidemic, especially during the first growth period, and (b) the uncertainty that characterizes the actual number of the asymptomatic infected cases in the total population (see e.g.

title:
  Tracing day-zero and forecasting the COVID-19 outbreak in Lombardy, Italy: A compartmental modelling and numerical optimization approach

abstract:
  INTRODUCTION: Italy became the second epicenter of the novel coronavirus disease 2019 (COVID-19) pandemic after China, surpassing by far China’s death toll. The disease swept through Lombardy, which remained in lockdown for about two months, starting from the 8th of March. As of that day, the isolation measures taken in Lombardy were extended to the entire country. Here, assuming that effectively there was one case “zero” that introduced the virus to the region, we provide estimates for: (a) the day-zero of the outbreak in Lombardy, Italy; (b) the actual number of asymptomatic infected cases in the total population until March 8; (c) the basic (R(0))and the effective reproduction number (R(e)) based on the estimation of the actual number of infected cases. To demonstrate the efficiency of the model and approach, we also provide a tentative forecast two months ahead of time, i.e. until May 4, the date on which relaxation of the measures commenced, on the basis of the COVID-19 Community Mobility Reports released by Google on March 29. METHODS: To deal with the uncertainty in the number of the actual asymptomatic infected cases in the total population Volpert et al. (2020), we address a modified compartmental Susceptible/ Exposed/ Infectious Asymptomatic/ Infected Symptomatic/ Recovered/ Dead (SEIIRD) model with two compartments of infectious persons: one modelling the cases in the population that are asymptomatic or experience very mild symptoms and another modelling the infected cases with mild to severe symptoms. The parameters of the model corresponding to the recovery period, the time from the onset of symptoms to death and the time from exposure to the time that an individual starts to be infectious, have been set as reported from clinical studies on COVID-19. For the estimation of the day-zero of the outbreak in Lombardy, as well as of the “effective” per-day transmission rate for which no clinical data are available, we have used the proposed SEIIRD simulator to fit the numbers of new daily cases from February 21 to the 8th of March. This was accomplished by solving a mixed-integer optimization problem. Based on the computed parameters, we also provide an estimation of the basic reproduction number R(0) and the evolution of the effective reproduction number R(e). To examine the efficiency of the model and approach, we ran the simulator to “forecast” the epidemic two months ahead of time, i.e. from March 8 to May 4. For this purpose, we considered the reduction in mobility in Lombardy as released on March 29 by Google COVID-19 Community Mobility Reports, and the effects of social distancing and of the very strict measures taken by the government on March 20 and March 21, 2020. RESULTS: Based on the proposed methodological procedure, we estimated that the expected day-zero was January 14 (min-max rage: January 5 to January 23, interquartile range: January 11 to January 18). The actual cumulative number of asymptomatic infected cases in the total population in Lombardy on March 8 was of the order of 15 times the confirmed cumulative number of infected cases, while the expected value of the basic reproduction number R(0) was found to be 4.53 (min-max range: 4.40- 4.65). On May 4, the date on which relaxation of the measures commenced the effective reproduction number was found to be 0.987 (interquartiles: 0.857, 1.133). The model approximated adequately two months ahead of time the evolution of reported cases of infected until May 4, the day on which the phase I of the relaxation of measures was implemented over all of Italy. Furthermore the model predicted that until May 4, around 20% of the population in Lombardy has recovered (interquartile range: ∼10% to ∼30%).


id: cord-348584-j3r2veou
title: Estimation of left behind subway passengers through archived data and video image processing
author: Sipetas, Charalampos
date: 2020-07-30
txt: ./txt/cord-348584-j3r2veou.txt
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mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0968090X20306422
doi: 10.1016/j.trc.2020.102727
summary:
  Image processing and object detection software was used to count the number of passengers that were left behind on station platforms from surveillance video feeds. By comparing this data against manual observations of the times that train doors open and close in the station, a linear regression model is estimated to predict dwell time from the train tracking records, as described in Section 5.1. To test the implementation of object detection with video in transit stations, a first step is to identify locations and times to collect video feeds as well as direct manual observations of left-behind passengers. Transportation Research Part C 118 (2020) 102727 shows a clear relationship between the video counts and passengers being left behind on station platforms, so there is potential to use the video feed as an explanatory variable in a model to estimate the likelihood of passengers being unable to board a train.

abstract:
  Crowding is one of the most common problems for public transportation systems worldwide, and extreme crowding can lead to passengers being left behind when they are unable to board the first arriving bus or train. This paper combines existing data sources with an emerging technology for object detection to estimate the number of passengers that are left behind on subway platforms. The methodology proposed in this study has been developed and applied to the subway in Boston, Massachusetts. Trains are not currently equipped with automated passenger counters, and farecard data is only collected on entry to the system. An analysis of crowding from inferred origin–destination data was used to identify stations with high likelihood of passengers being left behind during peak hours. Results from North Station during afternoon peak hours are presented here. Image processing and object detection software was used to count the number of passengers that were left behind on station platforms from surveillance video feeds. Automatically counted passengers and train operations data were used to develop logistic regression models that were calibrated to manual counts of left behind passengers on a typical weekday with normal operating conditions. The models were validated against manual counts of left behind passengers on a separate day with normal operations. The results show that by fusing passenger counts from video with train operations data, the number of passengers left behind during a day’s rush period can be estimated within [Formula: see text] of their actual number.


id: cord-350510-o4libq5d
title: On Linear Growth in COVID-19 Cases
author: Grinfeld, M.
date: 2020-06-22
url: http://medrxiv.org/cgi/content/short/2020.06.19.20135640v1?rss=1
doi: 10.1101/2020.06.19.20135640
abstract:
  We present an elementary model of COVID-19 propagation that makes explicit the connection between testing strategies and rates of transmission and the linear growth in new cases observed in many parts of the world. An essential feature of the model is that it captures the population-level response to the infection statistics information provided by governments and other organisations. The conclusions from this model have important implications regarding benefits of wide-spread testing for the presence of the virus, something that deserves greater attention.


id: cord-351430-bpv7p7zo
title: Air transportation, population density and temperature predict the spread of COVID-19 in Brazil
author: Pequeno, Pedro
date: 2020-06-03
txt: ./txt/cord-351430-bpv7p7zo.txt
cache: ./cache/cord-351430-bpv7p7zo.txt
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mime: text/plain
url: https://doi.org/10.7717/peerj.9322
doi: 10.7717/peerj.9322
summary:
  Further, we considered the following predictors: (1) time in days, to account for the exponential growth in case numbers during this period (Fig. 2) ; (2) number of arriving flights in the city''s metropolitan area in 2020, as airline connections can facilitate the spread of the virus (Ribeiro et al., 2020) ; (3) city population density, to account for facilitation of transmission under higher densities (Poole, 2020) ; (4) proportion of elderly people (≥60 years old) in the population, assuming that the elderly may be more likely to show severe symptoms of SARS-CoV-2 and, thus, to be diagnosed with COVID-19; (5) citizen mean income, which may affect the likelihood of people being infected by the virus, for example, due to limited access to basic sanitation or limited social isolation capabilities; (6) and the following meteorological variables: mean daily temperature ( C), mean daily solar radiation (kJ/m 2 ), mean daily relative humidity (%) and mean daily precipitation (mm).

abstract:
  There is evidence that COVID-19, the disease caused by the betacoronavirus SARS-CoV-2, is sensitive to environmental conditions. However, such conditions often correlate with demographic and socioeconomic factors at larger spatial extents, which could confound this inference. We evaluated the effect of meteorological conditions (temperature, solar radiation, air humidity and precipitation) on 292 daily records of cumulative number of confirmed COVID-19 cases across the 27 Brazilian capital cities during the 1st month of the outbreak, while controlling for an indicator of the number of tests, the number of arriving flights, population density, proportion of elderly people and average income. Apart from increasing with time, the number of confirmed cases was mainly related to the number of arriving flights and population density, increasing with both factors. However, after accounting for these effects, the disease was shown to be temperature sensitive: there were more cases in colder cities and days, and cases accumulated faster at lower temperatures. Our best estimate indicates that a 1 °C increase in temperature has been associated with a decrease in confirmed cases of 8%. The quality of the data and unknowns limit the analysis, but the study reveals an urgent need to understand more about the environmental sensitivity of the disease to predict demands on health services in different regions and seasons.


id: cord-351830-x4sv6ieu
title: Pandemic economics: optimal dynamic confinement under uncertainty and learning
author: Gollier, Christian
date: 2020-08-17
txt: ./txt/cord-351830-x4sv6ieu.txt
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url: https://www.ncbi.nlm.nih.gov/pubmed/32837397/
doi: 10.1057/s10713-020-00052-1
summary:
  In the absence of uncertainty, the optimal confinement policy is to impose a constant rate of lockdown until the suppression of the virus in the population. I show that introducing uncertainty about the reproduction number of deconfined people reduces the optimal initial rate of confinement. To illustrate, here is a short list of the sources of covid-19 uncertainties: The mortality rate, the rate of asymptomatic sick people, the rate of prevalence, the duration of immunity, the impact of various policies (lockdown, social distancing, compulsory masks, …) on the reproduction numbers, the proportion of people who could telework efficiently, and the possibility of cross-immunization from similar viruses. The uncertainty surrounding the reproduction number affects this expected cost because of the intricate non-linearities in the duration of the pandemic and in the sensitivity of the optimal future lockdown to new information.

abstract:
  Most integrated models of the covid pandemic have been developed under the assumption that the policy-sensitive reproduction number is certain. The decision to exit from the lockdown has been made in most countries without knowing the reproduction number that would prevail after the deconfinement. In this paper, I explore the role of uncertainty and learning on the optimal dynamic lockdown policy. I limit the analysis to suppression strategies where the SIR dynamics can be approximated by an exponential infection decay. In the absence of uncertainty, the optimal confinement policy is to impose a constant rate of lockdown until the suppression of the virus in the population. I show that introducing uncertainty about the reproduction number of deconfined people reduces the optimal initial rate of confinement.


id: cord-353318-12o3xniz
author: Ren, Zongyuan
date: 2020-05-01
url: https://doi.org/10.1016/j.cie.2020.106517
doi: 10.1016/j.cie.2020.106517
title:
  Generalized Z-numbers with hesitant fuzzy linguistic information and its application to medicine selection for the patients with mild symptoms of the COVID-19

abstract:
  Fuzzy set theory and a series of theories derived from it have been widely used to deal with uncertain phenomena in multi-criterion decision-making problems. However, few methods except the Z-number considered the reliability of information. In this paper, we propose a multi-criterion decision-making method based on the Dempster-Shafer (DS) theory and generalized Z-numbers. To do so, inspired by the concept of hesitant fuzzy linguistic term set, we extend the Z-number to a generalized form which is more in line with human expression habits. Afterwards, we make a bridge between the knowledge of Z-numbers and the DS evidence theory to integrate Z-valuations. The identification framework in the DS theory is used to describe the generalized Z-numbers to avoid ambiguity. Then, the knowledge of Z-numbers is used to derive the basic probability assignment of evidence and the synthetic rules in the DS theory are used to integrate evaluations. An illustrative example of medicine selection for the patients with mild symptoms of the COVID-19 is provided to show the effectiveness of the proposed method.


id: cord-354835-o0nscint
author: Roy, Sayak
date: 2020-06-04
url: https://doi.org/10.7759/cureus.8440
doi: 10.7759/cureus.8440
title:
  Epidemiological Determinants of COVID-19-Related Patient Outcomes in Different Countries and Plan of Action: A Retrospective Analysis

abstract:
  Current development around the pandemic of novel coronavirus disease 2019 (COVID-19) presents a significant healthcare resource burden threatening to overwhelm the available nationwide healthcare infrastructure. It is essential to consider, especially for resource-limited nations, strategizing the coordinated response to handle this crisis effectively and preparing for the upcoming emergence of calamity caused by this yet-to-know disease entity. Relevant epidemiological data were retrieved from currently available online reports related to COVID-19 patients. The correlation coefficient was calculated by plotting dependant variables - the number of COVID-19 cases and the number of deaths due to COVID 19 on the Y-axis and independent variables - critical-care beds per capita, the median age of the population of the country, the number of COVID-19 tests per million population, population density (persons per square km), urban population percentage, and gross domestic product (GDP) expense on health care - on the X-axis. After analyzing the data, both the fatality rate and the total number of COVID-19 cases were found to have an inverse association with the population density with the variable - the number of cases of COVID-19 - achieving a statistical significance (p-value 0.01). The negative correlation between critical care beds and the fatality rate is well-justified, as intensive care unit (ICU) beds and ventilators are the critical elements in the management of complicated cases. There was also a significant positive correlation between GDP expenses on healthcare by a country and the number of COVID-19 cases being registered (p-value 0.008), although that did not affect mortality (p-value 0.851). This analysis discusses the overview of various epidemiological determinants possibly contributing to the variation in patient outcomes across regions and helps improve our understanding to develop a plan of action and effective control measures in the future.


id: cord-355017-934v85q1
title: Serosurveys and convalescent plasma in COVID-19
author: Pérez-Cameo, Cristina
date: 2020-05-01
txt: ./txt/cord-355017-934v85q1.txt
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extension: .txt
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url: https://doi.org/10.1016/j.eclinm.2020.100370
doi: 10.1016/j.eclinm.2020.100370
abstract: nan
summary:
  Based on the WHO interim guidance developed for the 2014 Ebola outbreak [3] , convalescent plasma has advantages over other proposed treatment: it requires low technology (and therefore it can be produced where required independent of pharmaceutical companies), it is low cost and its production is easily scalable as long as there are sufficient donors. Furthermore, the real number of convalescent patients may be much greater than the number based on the recovery of previously identified patients because of the existence of asymptomatic and mild infections. Targeting populations at high risk of exposure such as contacts or health workers and self-identification of potentially convalescent patients using questionnaires could easily lead to as many plasma donors as required before the number of contagions peaks. Use of convalescent whole blood or plasma collected from patients recovered from Ebola virus disease for transfusion, as an empirical treatment during outbreaks.


id: cord-355201-pjoqahhk
title: Discrete simulation analysis of COVID-19 and prediction of isolation bed numbers
author: Li, X.
date: 2020-07-14
txt: ./txt/cord-355201-pjoqahhk.txt
cache: ./cache/cord-355201-pjoqahhk.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.07.13.20152330
doi: 10.1101/2020.07.13.20152330
summary:
  By adjusting or fitting necessary epidemic parameters, the effects of the following indicators on the development of the epidemic and the occupation of medical resources were explained: (1) incubation period, (2) response speed and detection capacity of the hospital, (3) disease cure time, and (4) population mobility. Through simulation, we show that the incubation period, response speed and detection capacity of the hospital, disease cure time, degree of population mobility, and infectivity of cured patients have different effects on the infectivity, scale, and duration of the epidemic. Among them, (1) incubation period, (2) response speed and detection capacity of the hospital, (3) disease cure time, and (4) population mobility have a significant impact on the demand and number of isolation beds (P <0.05), which agrees with the following regression equation: N = P * (-0.273 + 0.009I +0.234M + 0.012T1 + 0.015T2) * (1+V).

abstract:
  Background. The outbreak of COVID-19 has been defined by the World Health Organization as a pandemic, and containment depends on traditional public health measures. However, the explosive growth of the number of infected cases in a short period of time has caused tremendous pressure on medical systems. Adequate isolation facilities are essential to control outbreaks, so this study aims to quickly estimate the demand and number of isolation beds. Methods. We established a discrete simulation model for epidemiology. By adjusting or fitting necessary epidemic parameters, the effects of the following indicators on the development of the epidemic and the occupation of medical resources were explained: (1) incubation period, (2) response speed and detection capacity of the hospital, (3) disease cure time, and (4) population mobility. Finally, a method for predicting the reasonable number of isolation beds was summarized through multiple linear regression. Results. Through simulation, we show that the incubation period, response speed and detection capacity of the hospital, disease cure time, degree of population mobility, and infectivity of cured patients have different effects on the infectivity, scale, and duration of the epidemic. Among them, (1) incubation period, (2) response speed and detection capacity of the hospital, (3) disease cure time, and (4) population mobility have a significant impact on the demand and number of isolation beds (P <0.05), which agrees with the following regression equation: N = P * (-0.273 + 0.009I +0.234M + 0.012T1 + 0.015T2) * (1+V).


