Corpus overview


MeSH Disease

Human Phenotype


    displaying 41 - 50 records in total 823
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    Dynamics of SARS-CoV-2 with Waning Immunity in the UK Population

    Authors: Thomas Crellen; Li Pi; Emma Davis; Timothy M Pollington; Tim C D Lucas; Diepreye Ayabina; Anna Borlase; Jaspreet Toor; Kiesha Prem; Graham F Medley; Petra Klepac; T Deirdre Hollingsworth

    doi:10.1101/2020.07.24.20157982 Date: 2020-07-25 Source: medRxiv

    The dynamics of immunity are crucial to understanding the long-term patterns of the SARS-CoV-2 pandemic. While the duration and strength of immunity to SARS-CoV-2 is currently unknown, specific antibody SERO titres to related coronaviruses SARS-CoV and MERS-CoV have been shown to wane in recovered individuals, and immunity to seasonal circulating coronaviruses is estimated to be shorter than one year. Using an age TRANS-structured, deterministic model, we explore different potential immunity dynamics using contact data TRANS from the UK population. In the scenario where immunity to SARS-CoV-2 lasts an average of three months for non-hospitalised individuals, a year for hospitalised individuals, and the effective reproduction number TRANS (Rt) after lockdown is 1.2 (our worst case scenario), we find that the secondary peak occurs in winter 2020 with a daily maximum of 409,000 infectious individuals; almost three-fold greater than in a scenario with permanent immunity. Our models suggests that longitudinal serological surveys to determine if immunity in the population is waning will be most informative when sampling takes place from the end of the lockdown until autumn 2020. After this period, the proportion of the population with antibodies to SARS-CoV-2 SERO is expected to increase due to the secondary peak. Overall, our analysis presents considerations for policy makers on the longer term dynamics of SARS-CoV-2 in the UK and suggests that strategies designed to achieve herd immunity may lead to repeated waves of infection MESHD if immunity to re- infection MESHD is not permanent.

    An Analysis of Outbreak Dynamics and Intervention Effects for COVID-19 Transmission TRANS in Europe

    Authors: Wei Wang

    doi:10.1101/2020.07.21.20158873 Date: 2020-07-25 Source: medRxiv

    As of March 13, 2020, Europe became the center of COVID-19 pandemic. In order to prevent further spread and slow down the increase in confirmed cases TRANS and deaths MESHD, many countries in European Union have taken some interventions since mid-March. In this study, a metapopulation model was used to model the outbreak of COVID-19 in Europe and the effectiveness of these interventions were also estimated. The findings suggested that many countries successfully kept the reproduction number TRANS R_t less than 1 (e.g., Belgium, Germany, Spain, and France) while other countries exhibited R_t greater than 1 (e.g., United Kingdom, Cyprus). Based on the assumed reopen strategy, this study also revealed that a 2-week delay in response predicted approximately 2,000 deaths MESHD and 200,000 cases (daily peak value), while a 3-week delay predicted approximately 5,000 deaths MESHD and 600,000 cases (daily peak value). Therefore, a quick response upon signs of a re-emerging pandemic in the world is highly imperative to mitigate potential loss of life and to keep transmission TRANS of Covid-19 under control.

    Herd immunity thresholds for SARS-CoV-2 estimated from unfolding epidemics

    Authors: Ricardo Aguas; Rodrigo M. Corder; Jessica G. King; Guilherme Goncalves; Marcelo U. Ferreira; M. Gabriela M. Gomes

    doi:10.1101/2020.07.23.20160762 Date: 2020-07-24 Source: medRxiv

    As severe acute respiratory syndrome MESHD coronavirus 2 (SARS-CoV-2) spreads, the susceptible subpopulation declines causing the rate at which new infections MESHD occur to slow down. Variation in individual susceptibility or exposure to infection MESHD exacerbates this effect. Individuals that are more susceptible or more exposed tend to be infected and removed from the susceptible subpopulation earlier. This selective depletion of susceptibles intensifies the deceleration in incidence. Eventually, susceptible numbers become low enough to prevent epidemic growth or, in other words, the herd immunity threshold is reached. Here we fit epidemiological models with inbuilt distributions of susceptibility or exposure to SARS-CoV-2 outbreaks to estimate basic reproduction numbers TRANS ( R_0 TRANS) alongside coefficients of individual variation (CV) and the effects of containment strategies. Herd immunity thresholds are then calculated as 1-(1/ R_0 TRANS )^(1/((1+CV^2 ) )) or 1-(1/ R_0 TRANS )^(1/((1+2CV^2 ) )), depending on whether variation is on susceptibility or exposure. Our inferences result in herd immunity thresholds around 10-20%, considerably lower than the minimum coverage needed to interrupt transmission TRANS by random vaccination, which for R_0 TRANS higher than 2.5 is estimated above 60%. We emphasize that the classical formula, 1-1/ R_0 TRANS , remains applicable to describe herd immunity thresholds for random vaccination, but not for immunity induced by infection MESHD which is naturally selective. These findings have profound consequences for the governance of the current pandemic given that some populations may be close to achieving herd immunity despite being under more or less strict social distancing measures.

    The collective wisdom in the COVID-19 research: comparison and synthesis of epidemiological parameter estimates in preprints and peer-reviewed articles

    Authors: Yuejiao Wang; Zhidong Cao; Dajun Zeng; Qingpeng Zhang; Tianyi Luo

    doi:10.1101/2020.07.22.20160291 Date: 2020-07-24 Source: medRxiv

    Background Research papers related to COVID-19 have exploded. We aimed to explore the academic value of preprints through comparing with peer-reviewed publications, and synthesize the parameter estimates of the two kinds of literature. Method We collected papers regarding the estimation of four key epidemiological parameters of the COVID-19 in China: the basic reproduction number TRANS ( R0 TRANS), incubation period TRANS, infectious period TRANS, and case-fatality-rate (CFR). PubMed, Google Scholar, medRxiv, bioRxiv, arRxiv, and SSRN were searched by 20 March, 2020. Distributions of parameters and timeliness of preprints and peer-reviewed papers were compared. Further, four parameters were synthesized by bootstrap, and their validity was verified by susceptible-exposed-infectious-recovered-dead-cumulative (SEIRDC) model based on the context of China. Findings 106 papers were included for analysis. The distributions of four parameters in two literature groups were close, despite that the timeliness of preprints was better. Four parameter estimates changed over time. Synthesized estimates of R0 TRANS (3.18, 95% CI 2.85-3.53), incubation period TRANS (5.44 days, 95% CI 4.98-5.99), infectious period TRANS (6.25 days, 95% CI 5.09-7.51), and CFR (4.51%, 95% CI 3.41%-6.29%) were obtained from the whole parameters space, all with p<0.05. Their validity was evaluated by simulated cumulative cases of SEIRDC model, which matched well with the onset cases in China. Interpretation Preprints could reflect the changes of epidemic situation sensitively, and their academic value shouldn't be neglected. Synthesized results of literatures could reduce the uncertainty and be used for epidemic decision making. Funding The National Natural Science Foundation of China and Beijing Municipal Natural Science Foundation.

    COVID-19 and India: What Next?

    Authors: Ramesh Behl; Manit Mishra

    id:2007.13523v1 Date: 2020-07-24 Source: arXiv

    The study carries out predictive modeling based on publicly available COVID-19 data for the duration 01 April to 20 June 2020 pertaining to India and five of its most infected states: Maharashtra, Tamil Nadu, Delhi, Gujarat, and Rajasthan using susceptible, infected, recovered, and dead (SIRD) model. The basic reproduction number TRANS R0 TRANS is derived by exponential growth method using RStudio package R0 TRANS. The differential equations reflecting SIRD model have been solved using Python 3.7.4 on Jupyter Notebook platform. For visualization, Python Matplotlib 3.2.1 package is used. The study offers insights on peak-date, peak number of COVID-19 infections MESHD, and end-date pertaining to India and five of its states. The results could be leveraged by political leadership, health authorities, and industry doyens for policy planning and execution.

    A Framework for SARS-CoV-2 Testing on a Large University Campus: Statistical Considerations

    Authors: Paul J Rathouz; Catherine A Calder

    doi:10.1101/2020.07.23.20160788 Date: 2020-07-24 Source: medRxiv

    We consider testing strategies for active SARS-CoV-2 infection MESHD for a large university community population, which we define. Components of such a strategy include individuals tested because they self-select or are recommended for testing by a health care provider for their own health care; individuals tested because they belong to a high-risk group where testing serves to disrupt transmission TRANS; and, finally, individuals randomly selected for testing from the university community population as part of a proactive community testing, or surveillance, program. The proactive community testing program is predicated on a mobile device application that asks individuals to self-monitor COVID-like symptoms daily. The goals of this report are (i) to provide a framework for estimating prevalence SERO of SARS-CoV-2 infection MESHD in the university community wherein proactive community testing is a major component of the overall strategy, (ii) to address the issue of how many tests should be performed as part of the proactive community testing program, and (iii) to consider how effective proactive community testing will be for purposes of detection of new disease MESHD clusters. We argue that a comprehensive prevalence SERO estimate informed by all testing done of the university community is a good metric to obtain a global picture of campus SARS-CoV-2 infection MESHD rates at a particular point in time and to monitor the dynamics of infection MESHD over time, for example, estimating the population-level reproductive number TRANS, R0 TRANS). Importantly, the prevalence SERO metric can be useful to campus leadership for decision making. One example involves comparing campus prevalence SERO to that in the broader off-campus community. We also show that under some reasonable assumptions, we can obtain valid statements about the comprehensive prevalence SERO by only testing symptomatic persons in the proactive community testing component. The number of tests performed for individual-level and high-risk group-level needs will depend on the disease MESHD dynamics, individual needs, and testing availability. For purposes of this report, we assume that, for these groups of individuals, inferential precision --- that is, the accuracy with which we can estimate the true prevalence SERO from testing a random sample of individuals --- does not drive decisions on the number of tests. On the other hand, for proactive community testing, the desired level of inferential precision {in a fixed period of time can be used to justify the number of tests to perform {in that period. For example, our results show that, if we establish a goal of ruling out with 98\% confidence a background prevalence SERO of 2\% {in a given week, and the actual prevalence SERO is 1\% among those eligible for proactive community testing, we would need to test 835 randomly-selected symptomatics (i.e., those presenting with COVID-like symptoms) per week via the proactive community testing program in a campus of 80k individuals. In addition to justifying decisions about the number of tests to perform, inferential precision can formalize the intuition that testing of symptomatic individuals should be prioritized over testing asymptomatic TRANS individuals in the proactive community testing program.

    Effective Contact Tracing TRANS for COVID-19: A Systematic Review

    Authors: Carl-Etienne Juneau; Anne-Sara Briand; Tomas Pueyo; Pablo Collazzo; Louise Potvin

    doi:10.1101/2020.07.23.20160234 Date: 2020-07-24 Source: medRxiv

    Background: Contact tracing TRANS is commonly recommended to control outbreaks of COVID-19, but its effectiveness is unclear. This systematic review aimed to examine contact tracing TRANS effectiveness in the context of COVID-19. Methods: Following PRISMA guidelines, MEDLINE, Embase, Global Health, and All EBM Reviews were searched using a range of terms related to contact tracing TRANS for COVID-19. Articles were included if they reported on the ability of contact tracing TRANS to slow or stop the spread of COVID-19 or on characteristics of effective tracing TRANS efforts. Two investigators screened all studies. Results: A total of 32 articles were found. All were observational or modelling studies, so the quality of the evidence was low. Observational studies (n=14) all reported that contact tracing TRANS (alone or in combination with other interventions) was associated with better control of COVID-19. Results of modelling studies (n=18) depended on their assumptions. Under assumptions of prompt and thorough tracing TRANS with no further transmission TRANS, they found that contact tracing TRANS could stop an outbreak (e.g. by reducing the reproduction number TRANS from 2.2 to 0.57) or that it could reduce infections MESHD (e.g. by 24%-71% with a mobile tracing TRANS app). Under assumptions of slower, less efficient tracing TRANS, modelling studies suggested that tracing TRANS could slow, but not stop COVID-19. Conclusions: Observational and modelling studies suggest that contact tracing TRANS is associated with better control of COVID-19. Its effectiveness likely depends on a number of factors, including how many and how fast contacts are traced TRANS and quarantined, and how effective quarantines are at preventing further transmission TRANS. A cautious interpretation suggests that to stop the spread of COVID-19, public health practitioners have 2-3 days from the time a new case develops symptoms to isolate the case and quarantine at least 80% of its contacts, and that once isolated, cases and contacts should infect zero new cases. Less efficient tracing TRANS may slow, but not stop, the spread of COVID-19. Inefficient tracing TRANS (with delays of 4-5+ days or less than 60% of contacts quarantined with no further transmission TRANS) may not contribute meaningfully to control of COVID-19.

    Clinical Impact, Costs, and Cost-Effectiveness of Expanded SARS-CoV-2 Testing in Massachusetts

    Authors: Anne M Neilan; Elena Losina; Audrey C. Bangs; Clare Flanagan; Christopher Panella; G. Ege Eskibozkurt; Amir M. Mohareb; Emily P. Hyle; Justine A. Scott; Milton C. Weinstein; Mark J. Siedner; Krishna P Reddy; Guy Harling; Kenneth A. Freedberg; Fatma M. Shebl; Pooyan Kazemian; Andrea L. Ciaranello

    doi:10.1101/2020.07.23.20160820 Date: 2020-07-24 Source: medRxiv

    Background We projected the clinical and economic impact of alternative testing strategies on COVID-19 incidence and mortality in Massachusetts using a microsimulation model. Methods We compared five testing strategies: 1) PCR-severe-only: PCR testing only patients with severe/critical symptoms; 2) Self-screen: PCR-severe-only plus self-assessment of COVID-19-consistent symptoms with self-isolation if positive; 3) PCR-any-symptom: PCR for any COVID-19-consistent symptoms with self-isolation if positive; 4) PCR-all: PCR-any-symptom and one-time PCR for the entire population; and, 5) PCR-all-repeat: PCR-all with monthly re-testing. We examined effective reproduction numbers TRANS (Re, 0.9-2.0) at which policy conclusions would change. We used published data on disease progression MESHD and mortality, transmission TRANS, PCR sensitivity SERO/specificity (70/100%) and costs. Model-projected outcomes included infections MESHD, deaths MESHD, tests performed, hospital-days, and costs over 180-days, as well as incremental cost-effectiveness ratios (ICERs, $/quality-adjusted life-year [QALY]). Results In all scenarios, PCR-all-repeat would lead to the best clinical outcomes and PCR-severe-only would lead to the worst; at Re 0.9, PCR-all-repeat vs. PCR-severe-only resulted in a 63% reduction in infections MESHD and a 44% reduction in deaths MESHD, but required >65-fold more tests/day with 4-fold higher costs. PCR-all-repeat had an ICER

    Mathematical Modeling and Optimal Control Analysis of COVID-19 in Ethiopia

    Authors: Haileyesus Tessema Alemneh; Getachew Teshome Telahun

    doi:10.1101/2020.07.23.20160473 Date: 2020-07-24 Source: medRxiv

    In this paper we developed a deterministic mathematical model of the pandemic COVID-19 transmission TRANS in Ethiopia, which allows transmission TRANS by exposed humans. We proposed an SEIR model using system of ordinary differential equations. First the major qualitative analysis, like the disease MESHD free equilibruim point, endemic equilibruim point, basic reproduction number TRANS, stability analysis of equilibrium points and sensitivity SERO analysis was rigorously analysed. Second, we introduced time dependent controls to the basic model and extended to an optimal control model of the disease MESHD. We then analysed using Pontryagins Maximum Principle to derive necessary conditions for the optimal control of the pandemic. The numerical simulation indicated that, an integrated strategy effective in controling the epidemic and the gvernment must apply all control strategies in combating COVID-19 at short period of time.

    Surges in COVID-19 are led by lax government interventions in initial outbreaks

    Authors: Hsiang-Yu Yuan; Lindsey Wu; Dong-Ping Wang

    doi:10.1101/2020.07.17.20156604 Date: 2020-07-23 Source: medRxiv

    Sharp increases in COVID-19 cases occurred after reopening in the United States. We show that the post-intervention effective reproduction number TRANS is a strong predictor of the surge in late June. Lax interventions in the early stages coupled with elevated virus spread are primarily responsible for surges in most affected states.

The ZB MED preprint Viewer preVIEW includes all COVID-19 related preprints from medRxiv and bioRxiv, from ChemRxiv, from ResearchSquare, from arXiv and from and is updated on a daily basis (7am CET/CEST).



MeSH Disease
Human Phenotype

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