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Overview

MeSH Disease

Transmission

Seroprevalence
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    Regular universal screening for SARS-CoV-2 infection MESHD may not allow reopening of society after controlling a pandemic wave

    Authors: Martin CJ Bootsma; Mirjam E Kretzschmar; Ganna Rozhnova; Hans Heesterbeek; JAN J. A. J. W. kluytmans; Marc JM Bonten; Mayon Haresh Patel; Jade Stockham; Aisling O'Neill; Tristan Luke Clark; Tom Wilkinson; Paul Little; Nick A Francis; Gareth Griffiths; Michael Moore

    doi:10.1101/2020.11.18.20233122 Date: 2020-11-18 Source: medRxiv

    BackgroundTo limit societal and economic costs of lockdown measures, public health strategies are needed that control the spread of SARS-CoV-2 and simultaneously allow lifting of disruptive measures. Regular universal random screening of large proportions of the population regardless of symptoms has been proposed as a possible control strategy. MethodsWe developed a mathematical model that includes test sensitivity SERO depending on infectiousness for PCR-based and antigen-based tests, and different levels of onward transmission TRANS for testing and non-testing parts of the population. Only testing individuals participate in high- risk transmission TRANS events, allowing more transmission TRANS in case of unnoticed infection. We calculated the required testing interval and coverage to bring the effective reproduction number TRANS due to universal random testing (Rrt) below 1, for different scenarios of risk behavior of testing and non-testing individuals. FindingsWith R0 TRANS = 2.5, lifting all control measures for tested subjects with negative test results would require 100% of the population being tested every three days with a rapid test SERO method with similar sensitivity SERO as PCR-based tests. With remaining measures in place reflecting Re = 1.3, 80% of the population would need to be tested once a week to bring Rrt below 1. With lower proportions tested and with lower test sensitivity SERO, testing frequency should increase further to bring Rrt below 1. With similar Re values for tested and non-tested subjects, and with tested subjects not allowed to engage in higher risk events, at least 80% of the populations needs to test every five days to bring Rrt below. The impact of the test- sensitivity SERO on the reproduction number TRANS is far less than the frequency of testing. InterpretationRegular universal random screening followed by isolation of infectious individuals is not a viable strategy to reopen society after controlling a pandemic wave of SARS-CoV-2. More targeted screening approaches are needed to better use rapid testing SERO such that it can effectively complement other control measures. FundingRECOVER (H2020-101003589) (MJMB), ZonMw project 10430022010001 (MK, HH), FCT project 131_596787873 (GR). ZonMw project 91216062 (MK)

    Projections and fractional dynamics of COVID-19 MESHD with optimal control analysis

    Authors: Khondoker Nazmoon Nabi; Ellen Brooks-Pollock; Krasimira Tsaneva-Atanasova; Leon Danon; John Buresh; Mackenzie Edmondson; Peter A. Merkel; Ebbing Lautenbach; Rui Duan; Yong Chen; Liang Zhong; Angela SM Koh; Seow Yen Tan; Paul A Tambyah; Laurent Renia; Lisa F. P. Ng; David Chien Boon Lye; Christine Cheung; Sam T Douthwaite; Gaia Nebbia; Jonathan D Edgeworth; Ali R Awan; - The COVID-19 Genomics UK (COG-UK) consortium

    doi:10.1101/2020.11.17.20233031 Date: 2020-11-18 Source: medRxiv

    When the entire world is eagerly waiting for a safe, effective and widely available COVID-19 MESHD vaccine, un-precedented spikes of new cases are evident in numerous countries. To gain a deeper understanding about the future dynamics of COVID-19 MESHD, a compartmental mathematical model has been proposed in this paper incorporating all possible non-pharmaceutical intervention policies. Model parameters have been calibrated using sophisticated trust-region-reflective algorithm and short-term projection results have been illustrated for Argentina, Bangladesh, Brazil, Colombia and India. Control reproduction numbers TRANS ([R]c) have been calculated in order to get insights about the current epidemic scenario in the above-mentioned countries. Forecasting results depict that the aforesaid countries are having downward trends in daily COVID-19 MESHD cases. However, it is highly recommended to use efficacious face coverings and maintain strict physical distancing, as the pandemic is not over in any country. Global sensitivity SERO analysis enlightens the fact that efficacy of face coverings is the most significant parameter, which could significantly control the transmission TRANS dynamics of the novel coronavirus compared to other non-pharmaceutical measures. In addition, reduction in effective contact rate with isolated patients is also essential in bringing down the epidemic threshold ([R]c) below unity. All necessary graphical simulations have been performed with the help of Caputo-Fabrizio fractional derivatives. In addition, optimal control problem for fractional system has been designed and the existence of unique solution has also been showed by using Picard-Lindelof technique. Finally, the unconditionally stability of the given fractional numerical technique has been proved.

    Detecting COVID-19 MESHD infection hotspots in England using large-scale self-reported data from a mobile application

    Authors: Thomas Varsavsky; Mark S Graham; Liane S Canas; Sajaysurya Ganesh; Joan Capdevila Puyol; Carole H Sudre; Benjamin Murray; Marc Modat; M. Jorge Cardoso; Christina M Astley; David A Drew; Long H Nguyen; Tove Fall; Maria F Gomez; Paul W Franks; Andrew T Chan; Richard Davies; Jonathan Wolf; Claire J Steves; Tim D Spector; Sebastien Ourselin

    doi:10.1101/2020.10.26.20219659 Date: 2020-10-27 Source: medRxiv

    Background As many countries seek to slow the spread of COVID-19 MESHD without reimposing national restrictions, it has become important to track the disease at a local level to identify areas in need of targeted intervention. Methods We performed modelling on longitudinal, self-reported data from users of the COVID Symptom Study app in England between 24 March and 29 September, 2020. Combining a symptom-based predictive model for COVID-19 MESHD positivity and RT-PCR tests provided by the Department of Health we were able to estimate disease incidence, prevalence SERO and effective reproduction number TRANS. Geographically granular estimates were used to highlight regions with rapidly increasing case numbers, or hotspots. Findings More than 2.6 million app users in England provided 115 million daily reports of their symptoms, and recorded the results of 170,000 PCR tests. On a national level our estimates of incidence and prevalence SERO showed similar sensitivity SERO to changes as two national community surveys: the ONS and REACT studies. On a geographically granular level, our estimates were able to highlight regions before they were subject to local government lockdowns. Between 12 May and 29 September we were able to flag between 35-80% of regions appearing in the Government's hotspot list. Interpretation Self-reported data from mobile applications can provide a cost-effective and agile resource to inform a fast-moving pandemic, serving as an independent and complementary resource to more traditional instruments for disease surveillance.

    Evaluating the use of the reproduction number TRANS as an epidemiological tool, using spatio-temporal trends of the Covid-19 MESHD outbreak in England

    Authors: Katharine Sherratt; Sam Abbott; Sophie Meakin; Joel Hellewell; James D Munday; Nikos Bosse; - CMMID Covid-19 working group; Mark Jit; Sebastian Funk; Angela Fernandes; Ana M Dias; Ivan-Christian Kurolt; Alemka Markotic; Dragan Primorac; Adriana Soares; Luis Malheiro; Irena Trbojevic-Akmacic; Miguel Abreu; Rui Sarmento e Castro; Silvia Bettinelli; Annapaola Callegaro; Marco Arosio; Lorena Sangiorgio; Luca Lorini; Xavier Castells; Juan P Horcajada; Salome Pinho; Massimo Allegri; Clara Barrios; Gordan Lauc

    doi:10.1101/2020.10.18.20214585 Date: 2020-10-20 Source: medRxiv

    The time-varying reproduction number TRANS (Rt: the average number secondary infections TRANS caused by each infected person) may be used to assess changes in transmission TRANS potential during an epidemic. Since new infections usually are not observed directly, it can only be estimated from delayed and potentially biased data. We estimated Rt using a model that mapped unobserved infections to observed test-positive cases, hospital admissions, and deaths MESHD with confirmed Covid-19 MESHD, in seven regions of England over March through August 2020. We explored the sensitivity SERO of Rt estimates of Covid-19 MESHD in England to different data sources, and investigated the potential of using differences in the estimates to track epidemic dynamics in population sub-groups. Our estimates of transmission TRANS potential varied for each data source. The divergence between estimates from each source was not consistent within or across regions over time, although estimates based on hospital admissions and deaths MESHD were more spatio-temporally synchronous than compared to estimates from all test-positives. We compared differences in Rt with the demographic and social context of transmission TRANS, and found the differences between Rt may be linked to biased representations of sub-populations in each data source: from uneven testing rates, or increasing severity of disease with age TRANS, seen via outbreaks in care home populations and changing age TRANS distributions of cases. We highlight that policy makers should consider the source populations of Rt estimates. Further work should clarify the best way to combine and interpret Rt estimates from different data sources based on the desired use.

    SEAMHCRD deterministic compartmental model based on clinical stages of infection for COVID-19 MESHD pandemic in Sultanate of Oman

    Authors: Abraham Varghese; Shajidmon Kolamban; Vinu Sherimon; Eduardo M. Lacap Jr.; Saad Salman Ahmed; Jagath Prasad Sreedhar; Hasina Al Harthy; Huda Salim Al Shuaily

    doi:10.21203/rs.3.rs-91097/v1 Date: 2020-10-11 Source: ResearchSquare

    The present novel corona virus ( COVID-19 MESHD) infection has engendered a worldwide crisis across the world in an enormous scale within a very short period. The effective solution for this pandemic is to recognize the nature and spread of the disease TRANS so that appropriate policies can be framed. Mathematical modelling is always at the forefront to understand and provide an adequate description about the transmission TRANS of any disease. In this research work, we have formulated a deterministic compartmental model (SEAMHCRD) including various stages of infection, such as Mild, Moderate, Severe and Critical to study the spreading of COVID-19 MESHD and estimated the model parameters by fitting the model with the reported data of ongoing pandemic in Oman. The steady state, stability and final pandemic size of the model has been proved mathematically. The various transmission TRANS as well as transition parameters are estimated during the period from June 8th - July 30th, 2020. Based on the current estimated parameters, the pandemic size is also predicted for another 100 days. Sensitivity SERO analysis is performed to identify the key model parameters, and corresponding basic reproduction number TRANS has been computed using Next Generation Matrix (NGM) method. As the value of basic reproduction number TRANS ( R0 TRANS) is 0.9761 during the period from June 8th - July 30th, 2020, it is an indication for the policy makers to adopt appropriate remedial measures like social distancing and contact tracing TRANS to reduce the value of R0 TRANS to control the spread of the disease TRANS.

    Evaluating the Sensitivity SERO of SARS-CoV-2 Infection MESHD Rates on College Campuses to Wastewater Surveillance

    Authors: Tony E Wong; George M Thurston; Nathaniel Barlow; Nathan Cahill; Lucia Carichino; Kara Maki; David Ross; Jennifer Schneider; Judit Villar; Maria Luisa Sorli-Redo; Juan Pablo Horcajada; Natalia Garcia-Giralt; Julio Pascual; Juana Diez; Ruben Vicente; Robert Guerri-Fernandez; Zekaver Odabasi; Haner Direskeneli; Sait Karakurt; Ismail Cinel; Volkan Korten; Raquel Balleste; Simon Dellicour; Adriana Heguy; Ralf Duerr; Benjamin Skov Kaas-Hansen; Jon Middleton; Stine Hasling Mogensen; Hans Christian Thorsen-Meyer; Anders Perner; Mikkel Bonde; Alexander Bonde; Akshay Pai; Mads Nielsen; Martin Sillesen

    doi:10.1101/2020.10.09.20210245 Date: 2020-10-11 Source: medRxiv

    As college campuses reopen, we are in the midst of a large-scale experiment on the efficacy of various strategies to contain the SARS-CoV-2 virus. Traditional individual surveillance testing via nasal swabs and/or saliva is among the measures that colleges are pursuing to reduce the spread of the virus on campus. Additionally, some colleges are testing wastewater on their campuses for signs of infection, which can provide an early warning signal for campuses to locate COVID-positive individuals. However, a representation of wastewater surveillance has not yet been incorporated into epidemiological models for college campuses, nor has the efficacy of wastewater screening been evaluated relative to traditional individual surveillance testing, within the structure of these models. Here, we implement a new model component for wastewater surveillance within an established epidemiological model for college campuses. We use a hypothetical residential university to evaluate the efficacy of wastewater surveillance to maintain low infection MESHD rates. We find that wastewater sampling with a 1-day lag to initiate individual screening tests, plus completing the subsequent tests within a 4-day period can keep overall infections within 5% of the infection rates seen with traditional individual surveillance testing. Our results also indicate that wastewater surveillance can be an effective way to dramatically reduce the number of false positive cases by identifying subpopulations for surveillance testing where infectious individuals are more likely to be found. Through a Monte Carlo risk analysis, we find that surveillance testing that relies solely on wastewater sampling can be fragile against scenarios with high viral reproductive numbers TRANS and high rates of infection of campus community members by outside sources. These results point to the practical importance of additional surveillance measures to limit the spread of the virus on campus and the necessity of a proactive response to the initial signs of outbreak.

    Scrutinizing the Spread of Covid-19 MESHD in Madagascar

    Authors: Stephan Narison; Stavros Maltezos; Ivan Sola; Gabriel Rada; Mohammad-Javad Khosousi; Leila Khosousi; Hosein Ameri; Morteza Arab-Zozani; Xijing Zhang; Duolao Wang; Yi Liu; Ling Tao; Jean-Francois Carod; Stephanie Eustache; Celine Tourbillon; Elodie Boizon; Samantha James; Felix Djossou; Henrik Salje; Simon Cauchemez; Dominique Rousset; Ana F. Bernardes; Thyago A. Nunes; Luciana C. Ribeiro; Marcus V. Agrela; Maria Luiza Moretti; Lucas I. Buscaratti; Fernanda Crunfli; Raissa . G Ludwig; Jaqueline A. Gerhardt; Renata Seste-Costa; Julia Forato; Mariene . R Amorin; Daniel A. T. Texeira; Pierina L. Parise; Matheus C. Martini; Karina Bispo-dos-Santos; Camila L. Simeoni; Fabiana Granja; Virginia C. Silvestrini; Eduardo B. de Oliveira; Vitor M. Faca; Murilo Carvalho; Bianca G. Castelucci; Alexandre B. Pereira; Lais D. Coimbra; Patricia B. Rodrigues; Arilson Bernardo S. P. Gomes; Fabricio B. Pereira; Leonilda M. B. Santos; Andrei C. Sposito; Robson F. Carvalho; Andre S. Vieira; Marco A. R. Vinolo; Andre Damasio; Licio A. Velloso; Helder I. Nakaya; Henrique Marques-Souza; Rafael E. Marques; Daniel Martins-de-Souza; Munir S. Skaf; Jose Luiz Proenca-Modena; Pedro M. Moraes-Vieira; Marcelo A. Mori; Alessandro S. Farias

    doi:10.1101/2020.09.27.20202556 Date: 2020-09-28 Source: medRxiv

    We scrutinize the evolution of Covid-19 MESHD in Madagascar by comparing results from three approaches (cubic polynomial, semi-gaussian and gaussian-like models) which we use to provide an analytical form of the spread of the pandemic. In so doing, we introduce (for the first time) the ratio R^(c,d)_{I/T} of the cumulative and daily numbers of infected persons over the corresponding one of tests which are expected to be less sensitive to the number of the tests because the credibility of the results based only on the absolute numbers often raises some criticisms. We also give and compare the reproduction number TRANS R_eff from different approaches and with the ones of some European countries with a small number of population (Greece, Switzerland) and some other African countries. Finally, we show the evolution of the per cent number of cured persons and the total number of deaths from which we deduce some comments on the performance SERO of medical cares.

    Mathematical modelling and optimal cost-effective control of COVID-19 MESHD transmission TRANS dynamics

    Authors: S. Olaniyi; O.S. Obabiyi; K.O. Okosun; A.T. Oladipo; S.O. Adewale

    doi:10.21203/rs.3.rs-84331/v1 Date: 2020-09-27 Source: ResearchSquare

    The novel coronavirus disease MESHD ( COVID-19 MESHD) caused by a new strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains the current global health challenge. In this paper, an epidemic model based on system of ordinary differential equations is formulated by taking into account the transmission TRANS routes from symptomatic, asymptomatic TRANS and hospitalized individuals. The model is fitted to the corresponding cumulative number of hospitalized individuals (active cases) reported by the Nigeria Centre for Disease Control (NCDC), and parameterized using the least squares method. The basic reproduction number TRANS which measures the potential spread of COVID-19 MESHD in the population is computed using the next generation operator method. Further, Lyapunov function is constructed to investigate the stability of the model around a disease-free equilibrium point. It is shown that the model has a globally asymptotically stable disease-free equilibrium if the basic reproduction number TRANS of the novel coronavirus transmission TRANS is less than one. Sensitivities SERO of the model to changes in parameters are explored. It is revealed further that the basic reproduction number TRANS can be brought to a value less than one in Nigeria, if the current effective transmission TRANS rate of the disease can be reduced by 50%. Otherwise, the number of active cases may get up to 2.5% of the total estimated population. In addition, two time-dependent control variables, namely preventive and management measures, are considered to mitigate the damaging effects of the disease using Pontryagin's maximum principle. The most cost-effective control measure is determined through cost-effectiveness analysis. Numerical simulations of the overall system are implemented in MatLab® for demonstration of the theoretical results.

    Efficient calibration for imperfect epidemic models with applications to the analysis of COVID-19 MESHD

    Authors: Chih-Li Sung; Ying Hung

    id:2009.12523v1 Date: 2020-09-26 Source: arXiv

    The estimation of unknown parameters in simulations, also known as calibration, is crucial for practical management of epidemics and prediction of pandemic risk. A simple yet widely used approach is to estimate the parameters by minimizing the sum of the squared distances between actual observations and simulation outputs. It is shown in this paper that this method is inefficient, particularly when the epidemic models are developed based on certain simplifications of reality, also known as imperfect models which are commonly used in practice. To address this issue, a new estimator is introduced that is asymptotically consistent, has a smaller estimation variance than the least squares estimator, and achieves the semiparametric efficiency. Numerical studies are performed to examine the finite sample performance SERO. The proposed method is applied to the analysis of the COVID-19 MESHD pandemic for 20 countries based on the SEIR (Susceptible-Exposed-Infectious-Recovered) model with both deterministic and stochastic simulations. The estimation of the parameters, including the basic reproduction number TRANS and the average incubation period TRANS, reveal the risk of disease outbreaks in each country and provide insights to the design of public health interventions.

    COVID-19 MESHD dynamics across the US: A deep learning study of human mobility and social behavior

    Authors: Mohamed Aziz Bhouri; Francisco Sahli Costabal; Hanwen Wang; Kevin Linka; Mathias Peirlinck; Ellen Kuhl; Paris Perdikaris; Philippa C Matthews; Jienchi Dorward; Bernhard Graf; Florian Hitzenbichler; Frank Hanses; Hendrik Poeck; Marina Kreutz; Evelyn Orso; Ralph Burkhardt; Tanja Niedermair; Christoph Brochhausen; Andre Gessner; Bernd Salzberger; Matthias Mack; Christine Goffinet; Florian Kurth; Martin Witzenrath; Maria Theresa Völker; Sarah Dorothea Müller; Uwe Gerd Liebert; Naveed Ishaque; Lars Kaderali; Leif Erik Sander; Sven Laudi; Christian Drosten; Roland Eils; Christian Conrad; Ulf Landmesser; Irina Lehmann

    doi:10.1101/2020.09.20.20198432 Date: 2020-09-23 Source: medRxiv

    This paper presents a deep learning framework for epidemiology system identification from noisy and sparse observations with quantified uncertainty. The proposed approach employs an ensemble of deep neural networks to infer the time-dependent reproduction number TRANS of an infectious disease MESHD by formulating a tensor-based multi-step loss function that allows us to efficiently calibrate the model on multiple observed trajectories. The method is applied to a mobility and social behavior-based SEIR model of COVID-19 MESHD spread. The model is trained on Google and Unacast mobility data spanning a period of 66 days, and is able to yield accurate future forecasts of COVID-19 MESHD spread in 203 US counties within a time-window of 15 days. Strikingly, a sensitivity SERO analysis that assesses the importance of different mobility and social behavior parameters reveals that attendance of close places, including workplaces, residential, and retail and recreational locations, has the largest impact on the basic reproduction number TRANS. The model enables us to rapidly probe and quantify the effects of government interventions, such as lock-down and re-opening strategies. Taken together, the proposed framework provides a robust workflow for data-driven epidemiology model discovery under uncertainty and produces probabilistic forecasts for the evolution of a pandemic that can judiciously inform policy and decision making. All codes and data accompanying this manuscript are available at https://github.com/PredictiveIntelligenceLab/Deep COVID19 MESHD.

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MeSH Disease
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Seroprevalence


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