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MeSH Disease


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    A COVID-19 MESHD Model for Local Authorities of the United Kingdom

    Authors: Swapnil Mishra; Jamie Scott; Harrison Zhu; Neil M Ferguson; Samir Bhatt; Seth Flaxman; Axel Gandy; Fredrik Nyberg; Waheed-Ul-Rahman Ahmed; Osaid Alser; Heba Alghoul; Thamir Alshammari; Lin Zhang; Paula Casajust; Carlos Areia; Karishma Shah; Christian Reich; Clair Blacketer; Alan Andryc; Stephen Fortin; Karthik Natarajan; Mengchun Gong; Asieh Golozar; Daniel Morales; Peter Rijnbeek; Vignesh Subbian; Elena Roel; Martina Recalde; Jennifer C.E. Lane; David Vizcaya; Jose D. Posada; Nigam H. Shah; Jitendra Jonnagaddala; Lana Yin Hui Lai; Francesc Xavier Aviles-Jurado; George Hripcsak; Marc A. Suchard; Otavio T. Ranzani; Patrick Ryan; Daniel Prieto-Alhambra; Kristin Kostka; Talita Duarte-Salles

    doi:10.1101/2020.11.24.20236661 Date: 2020-11-27 Source: medRxiv

    We propose and describe a model for the COVID-19 MESHD epidemic of the United Kingdom at the level of local authorities. The model fits within a general framework for semi-mechanistic Bayesian models of the epidemic, with some important innovations: for example, we estimate the proportion of infections resulting in deaths MESHD and reported cases and we model the infections explicitly as random variables. The model is designed to be updated daily based on publicly available data. We envisage the model to be useful for short term projections of the epidemic over the next few weeks and to estimate past local values such as the reproduction number TRANS of the epidemic in the past. The model fits are available on a public website, covid19 MESHDlocal. The model is currently being used by the Scottish government in their decisions on interventions within Scotland [1,issue 24 to now]

    A Time-dependent mathematical model for COVID-19 MESHD transmission TRANS dynamics and analysis of critical and hospitalized cases with bed requirements

    Authors: Avaneesh Singh; Manish Kumar Bajpai; Shyam Lal Gupta; Hebatallah M Hassan; Mariam T Amin; Radwa K Soliman; Alaa A Attia; Amro A Zarzour; Mohamed Zain; Aliae Mohamed-Hussein; Maiada K Hashem; Sahar M Hassany; Ahmed Aly; Ahmed Shoap; Mostafa Saber; Ron Kusters; Martin Schuijt; Marc H. M. Thelen; Nigel French; Edward C Holmes; Joep de Ligt

    doi:10.1101/2020.10.28.20221721 Date: 2020-11-03 Source: medRxiv

    A time-dependent SEAIHCRD model is the extension of the SEIR model, which includes some new compartment that is asymptomatic TRANS infectious people, hospitalized people, critical people, and dead compartments. In this article, we analyzed six countries, namely the United States, Brazil, India, South Africa, Russia, and Mexico. A time-dependent SEAIHCRD model calculates the magnitude of peaks for exposed people, asymptomatic TRANS infectious people, symptomatic infectious people, hospitalized people, the number of people admitted to ICUs, and the number of COVID-19 MESHD deaths over time. It also computes the spread scenario and endpoints of disease. The proposed model also involves asymptomatic TRANS infectious individuals. To estimate the various parameters, we first collect the data and fit that using the Lavenberg-Marquardt model for death MESHD cases. Then we calculate infection rate, recovery rate, case fatality rate, and the basic reproduction number TRANS over time. We calculate two types of case fatality rates: one is the daily case fatality rate, and the other is the total case fatality rate. The proposed model includes the social distance parameter, various age TRANS classes, hospital beds for severe cases, and ICU beds or ventilators for critical cases. This model will be useful to determine various essential parameters such as daily hospitalization rate, daily death MESHD rates, including the requirement of normal and ICU beds during peak days of infection.

    Simulation and prediction of further spread of COVID-19 MESHD in The Republic of Serbia by SEIRDS model of disease transmission TRANS

    Authors: Slavoljub Grozdan Stanojevic; Mirza Ponjavic; Slobodan Stanojevic; Aleksandar Stevanovic; Sonja Radojicic; Beatriz Perazzi; Sergio Villordo; Diego Alvarez; - BioBanco Working Group; Marcela Echavarria; Kasopefoluwa Y. Oguntuyo; Christian Stevens; Benhur Lee; Jorge Carradori; Julio Caramelo; Marcelo Yanovsky; Andrea Gamarnik; Bart N Lambrecht; Lynda Coughlan; Adolfo Garcia-Sastre; Bruno G De Geest; Michael Schotsaert; Marion Yger; Bertrand Degos; Louise-Laure Mariani; Christophe Bouche; Nathalie Dzierzynski; Bruno Oquendo; Flora Ketz; An-Hung Nguyen; Aurelie Kas; Jean-Yves Delattre; Jean-Christophe Corvol

    doi:10.1101/2020.10.21.20216986 Date: 2020-10-23 Source: medRxiv

    As a response to the pandemic caused by SARSCov-2 virus, on 15 March, 2020, the Republic of Serbia introduced comprehensive anti-epidemic measures to curb COVID 19. After a slowdown in the epidemic, on 6 May, 2020, the regulatory authorities decided to relax the implemented measures. However, the epidemiological situation soon worsened again. As of 15 October, 2020, a total of 35,454 cases of SARSCov-2 infection MESHD have been reported in Serbia, including 770 deaths MESHD caused by COVID19 MESHD. In order to better understand the epidemic dynamics and predict possible outcomes, we have developed a mathematical model SEIRDS (S-susceptible, E-exposed, I-infected MESHD, R-recovered, D-dead due to COVID19 MESHD infection, S-susceptible). When developing the model, we took into account the differences between different population strata, which can impact the disease dynamics and outcome. The model can be used to simulate various scenarios of the implemented intervention measures and calculate possible epidemic outcomes, including the necessary hospital capacities. Considering promising results regarding the development of a vaccine against COVID19 MESHD, the model is enabled to simulate vaccination among different population strata. The findings from various simulation scenarios have shown that, with implementation of strict measures of contact reduction, it is possible to control COVID19 MESHD and reduce number of deaths MESHD. The findings also show that limiting effective contacts within the most susceptible population strata merits a special attention. However, the findings also show that the disease has a potential to remain in the population for a long time, likely with a seasonal pattern. If a vaccine, with efficacy equal or higher than 65%, becomes available it could help to significantly slow down or completely stop circulation of the virus in human population. The effects of vaccination depend primarily on: 1. Efficacy of available vaccine(s), 2. Prioritization of the population categories for vaccination, and 3. Overall vaccination coverage of the population, assuming that the vaccine(s) develop solid immunity in vaccinated individuals. With expected basic reproduction number TRANS of Ro=2.46 and vaccine efficacy of 68%, an 87%- coverage would be sufficient to stop the virus circulation.

    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.

    Mathematical Perspective of Covid-19 MESHD Pandemic: Disease Extinction Criteria in Deterministic and Stochastic Models

    Authors: Debadatta Adak; Abhijit Majumder; Nandadulal Bairagi; David Shackleton; Melanie Jensen; Mariana O. Diniz; Nathalie M. Schmidt; David K. Butler; Oliver E. Amin; Sasha N. L. Bailey; Stephen Talyor; Jessica Jones; Meleri Jones; Wing Yiu Jason Lee; Joshua Rosenheim; Aneesh Chandran; George Joy; Cecilia Di Genova; Nigel J. Temperton; Jonathan Lambourne; Teresa Cutino-Moguel; Mervyn Andiapen; Marianna Fontana; Angelique Smit; Amanda Semper; Ben O'Brien; Benjamin Chain; Tim Brooks; Charlotte Manisty; Thomas Treibel; James Moon; - COVIDsortium Investigators; Mahdad C. Noursadeghi; - COVIDsortium Immune correlates network; Daniel M Altmann; Mala K. Mani; Aine McKnight; Rosemary J. Boyton; DANIEL PRIETO-ALHAMBRA

    doi:10.1101/2020.10.12.20211201 Date: 2020-10-14 Source: medRxiv

    The world has been facing the biggest virological invasion in the form of Covid-19 MESHD pandemic since the beginning of the year 2020. In this paper, we consider a deterministic epidemic model of four compartments classified based on the health status of the populations of a given country to capture the disease progression. A stochastic extension of the deterministic model is further considered to capture the uncertainty or variation observed in the disease transmissibility TRANS. In the case of a deterministic system, the disease-free equilibrium will be globally asymptotically stable if the basic reproduction number TRANS is less than unity, otherwise, the disease persists. Using Lyapunov functional methods, we prove that the infected population of the stochastic system tends to zero exponentially almost surely if the basic reproduction number TRANS is less than unity. The stochastic system has no interior equilibrium, however, its asymptotic TRANS solution is shown to fluctuate around the endemic equilibrium of the deterministic system under some parametric restrictions, implying that the infection persists. A case study with the Covid-19 MESHD epidemic data of Spain is presented and various analytical results have been demonstrated. The epidemic curve in Spain clearly shows two waves of infection. The first wave was observed during March-April and the second wave started in the middle of July and not completed yet. A real-time basic reproduction number TRANS has been given to illustrate the epidemiological status of Spain throughout the study period. Estimated cumulative numbers of confirmed and death MESHD cases are 1,613,626 and 42,899, respectively, with case fatality rate 2.66 per cent till the deadly virus is eliminated from Spain.

    COVID-19 MESHD Outbreaks in Refugee Camps. A simulation study.

    Authors: CARLOS M HERNANDEZ-SUAREZ; Paolo Verme; Sergiy Radyakin; Efren Murillo-Zamora; Lara Murphy Jones; Michele Donato; Yiran Liu; Yapeng Su; Minas Karagiannis; Theodoros Marantos; Yehudit Hasin-Brumshtein; Yudong D He; Evangelos J Giamarellos-Bourboulis; Jim Heath; Purvesh Khatri

    doi:10.1101/2020.10.02.20204818 Date: 2020-10-05 Source: medRxiv

    We built a mathematical model for SARS-CoV-2 transmission TRANS and analyze it using both a deterministic and a stochastic approach. We used this model to project the burden of the disease in refugee camps characterized by peculiar demographic characteristics and a high level of deprivation, including lack of medical facilities and personnel, as well as limited possibility to implement containment and quarantine measures. Most of the parameters in our model were adapted from published literature but we used our own estimates of the basic reproduction number TRANS, $ R_0 TRANS$ as well as the lethality by age group TRANS and gender TRANS. We projected the burden in terms of number of infections, number of deaths MESHD and number of bed-days in hospitalization and intensive care, among others. We conclude that the harsh conditions of refugee camps combined with a high share of young people leads to a relatively mild scenario for the burden of the disease.

    Shielding the vulnerable in an epidemic: a numerical approach

    Authors: Guus Balkema

    id:2010.00959v1 Date: 2020-10-01 Source: arXiv

    The death toll for Covid-19 MESHD may be reduced by dividing the population into two classes, the vulnerable and the fit, with different lockdown regimes. Instead of one reproduction number TRANS there now are four parameters. These make it possible to quantify the effect of the social distancing measures. There is a simple stochastic model for epidemics in a two type population. Apart from the size of the population of the vulnerable and the fit, and the initial number of infected in the two classes, only the four reproduction parameters are needed to run the two type Reed-Frost model. The program is simple and fast. On a pc it takes less than five minutes to do a hundred thousand simulations of the epidemic for a population of the size of the US. Epidemics are non-linear processes. Results may be counterintuitive. The average number of vulnerable persons infected by an infectious fit person is a crucial parameter of the epidemic in the two type population. Intuitively this parameter should be small. However simulations show that even if this parameter is small the death MESHD toll may be higher than without shielding. Under certain conditions increasing the value of the parameter may reduce the death MESHD toll. The article addresses these blind spots in our intuition.

    Mitigating the transmission TRANS of infection and death MESHD due to SARS-CoV-2 through non-pharmaceutical interventions and repurposing drugs

    Authors: Chittaranjan Mondal; Debadatta Adak; Abhijit Majumder; Nandadulal Bairagi; Travis Sanchez; Daniel Westreich; Julia L Marcus; Matthew Richardson; Erica Ryke; Hong Xie; Lasata Shrestha; Amin Addetia; Victoria M Rachleff; Nicole Lieberman; Meei-Li Huang; Romesh Gautom; Geoff Melly; Brian Hiatt; Philip Dykema; Amanda Adler; Elisabeth Brandstetter; Peter D. Han; Kairsten Fay; Misja Ilcisin; Kirsten Lacombe; Thomas R Sibley; Melissa Truong; Caitlin R Wolf; Michael Boeckh; Janet A Englund; Michael Famulare; Barry R Lutz; Mark J Rieder; Matthew Thompson; Jeffrey S Duchin; Lea M Starita; Helen Y Chu; Jay Shendure; Keith R Jerome; Scott Lindquist; Alex Greninger; Deborah A Nickerson; Trevor Bedford

    doi:10.1101/2020.09.28.20202804 Date: 2020-09-30 Source: medRxiv

    The Covid-19 MESHD pandemic has put the world under immeasurable stress. There is no specific drug or vaccine that can cure the infection or protect people from the infection of coronavirus. It is therefore prudent to use the existing resources and control strategies in an optimal way to contain the virus spread and provide the best possible treatments to the infected individuals. Use of the repurposing drugs along with the non-pharmaceutical intervention strategies may be the right way for fighting against the ongoing pandemic. It is the objective of this work to demonstrate through mathematical modelling and analysis how and to what extent such control strategies can improve the overall Covid-19 MESHD epidemic burden. The criteria for disease elimination & persistence were established through the basic reproduction number TRANS. A case study with the Indian Covid-19 MESHD epidemic data is presented to visualize and illustrate the effects of lockdown, maintaining personal hygiene & safe distancing, and repurposing drugs. It is shown that India can significantly improve the overall Covid-19 MESHD epidemic burden through the combined use of NPIs and repurposing drugs though containment of spreading is difficult without serious community participation.

    Exploring COVID-19 MESHD Daily Records of Diagnosed Cases and Fatalities Based on Simple Non-parametric Methods

    Authors: Hans H. Diebner; Nina Timmesfeld

    id:10.20944/preprints202009.0628.v1 Date: 2020-09-26 Source:

    Based on comprehensible non-parametric methods, estimates of crucial parameters that characterise the COVID-19 MESHD pandemic with a focus on the German epidemic are presented. Where appropriate, the estimates for Germany are compared with the results for six other countries (FR, IT, US, UK, ES, CH) to get an idea of the breadth of applicability and a relational understanding. Thereby, only prevalence SERO data of daily reported new counts of diagnosed cases and fatalities provided by the ECDC are used. Where appropriate, the results are compared with conclusions drawn from using the dataset provided by the RKI. Drawing on uncertain a priori knowledge is avoided. Specifically, we present estimates for the duration from diagnosis to death MESHD being 13 days for Germany and about 2 days for Italy as the extremes. Furthermore, based on the knowledge of this time lag between diagnoses and deaths, properly delayed asymptotic TRANS as well as instantaneous fatality-case ratios are calculated having superiority compared to the commonly published case-fatality rate. The median of the time series of the instantaneous fatality-case ratio with proper delay of 13-days between cases and deaths for Germany turns out to be 0.024. Asymptotic TRANS values are presented for other countries with France ranking highest with a fatality-case ratio of almost 0.2 at its peak. The basic reproduction number TRANS, R_0 TRANS, for Germany is estimated to be between 2.4 and 3.4. The uncertainty stems from uncertain knowledge of the generation time. A delay autocorrelation shows resonances at about 4 days and 7 days, where the latter resonance is at least partially attributable to the sampling process with weekly periodicity. The calculation of the basic reproduction number TRANS is based on an evaluation of cumulative numbers of cases yielding time-dependent doubling times as an intermediate step. This allows to infer to the reproduction number TRANS during the early phase of onset of the epidemic. In a second approach, the instantaneous basic reproduction number TRANS is derived from the incident (counts of new) cases and allows, in contrast to the first version, to infer to the temporal behaviour of the reproduction number TRANS during the later epidemic course. To conclude, by avoiding complicated parametric models we provide insights into basic features of the COVID-19 MESHD epidemic in an utmost transparent and comprehensible way.


    Authors: Ritwik Bhaduri; Ritoban Kundu; Soumik Purkayastha; Mike Kleinsasser; Lauren J Beesley; Bhramar Mukherjee; Robert Avram; Geoffrey Tison; David Wen; Xochitl Butcher; Helena Eitel; Mark Pletcher; Dilcia Sambrano; Yamitzel Zaldivar; Danilo Franco; Sandra Lopez Verges; Dexi Zhang; Fanjing Fan; Baojun Wang; Xavier Saez Llorens; Rodrigo DeAntonio; Ivonne Torres-Atencio; Eduardo Ortega-Barria; Rao Kosagisharaf; Ricardo Lleonart; Li Chong; Amador Goodridge; - COVID-19 SEROLOGY COLLABORATOR GROUP

    doi:10.1101/2020.09.24.20200238 Date: 2020-09-25 Source: medRxiv

    The false negative rate of the diagnostic RT-PCR test for SARS-CoV-2 has been reported to be substantially high. Due to limited availability of testing, only a non-random subset of the population can get tested. Hence, the reported test counts are subject to a large degree of selection bias. We consider an extension of the Susceptible-Exposed-Infected-Removed (SEIR) model under both selection bias and misclassification. We derive closed form expression for the basic reproduction number TRANS under such data anomalies MESHD using the next generation matrix method. We conduct extensive simulation studies to quantify the effect of misclassification and selection on the resultant estimation and prediction of future case counts. Finally we apply the methods to reported case-death-recovery count data from India, a nation with more than 5 million cases reported over the last seven months. We show that correcting for misclassification and selection can lead to more accurate prediction of case-counts (and death counts) using the observed data as a beta tester. The model also provides an estimate of undetected infections and thus an under-reporting factor. For India, the estimated under-reporting factor for cases is around 21 and for deaths MESHD is around 6. We develop an R-package (SEIRfansy) for broader dissemination of the methods.

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MeSH Disease

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