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MeSH Disease

Human Phenotype

There are no HP terms in the subcorpus


Transmission

Seroprevalence
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    A robust nonlinear mixed-effects model for COVID-19 deaths MESHD data

    Authors: Fernanda L. Schumacher; Clecio S. Ferreira; Marcos O. Prates; Alberto Lachos; Victor H. Lachos

    id:2007.00848v2 Date: 2020-07-02 Source: arXiv

    The analysis of complex longitudinal data such as COVID-19 deaths MESHD is challenging due to several inherent features: (i) Similarly-shaped profiles with different decay patterns; (ii) Unexplained variation among repeated measurements within each country, these repeated measurements may be viewed as clustered data since they are taken on the same country at roughly the same time; (iii) Skewness, outliers or skew-heavy-tailed noises are possibly embodied within response variables. This article formulates a robust nonlinear mixed-effects model based in the class of scale mixtures of skew-normal distributions for modeling COVID-19 deaths MESHD, which allows the analysts to model such data in the presence of the above described features simultaneously. An efficient EM-type algorithm is proposed to carry out maximum likelihood estimation of model parameters. The bootstrap method is used to determine inherent characteristics of the nonlinear individual profiles such as confidence interval of the predicted deaths MESHD and fitted curves. The target is to model COVID-19 deaths MESHD curves from some Latin American countries since this region is the new epicenter of the disease MESHD. Moreover, since a mixed-effect framework borrows information from the population-average TRANS effects, in our analysis we include some countries from Europe and North America that are in a more advanced stage of their COVID-19 deaths MESHD curve.

    An Agent Based Model methodology for assessing spread and health systems burden for Covid-19 using a synthetic population from India

    Authors: Narassima MS; Guru Rajesh Jammy; Rashmi Pant; Lincoln Choudhury; Aadharsh R; Vijay Yeldandi; Anbuudayasankar SP; Rangasami P

    doi:10.1101/2020.06.04.20121848 Date: 2020-06-05 Source: medRxiv

    Covid-19 disease MESHD, caused by SARS-CoV-2 virus, has infected over four million people globally. It has been declared as a global public health emergency MESHD by the World Health Organization. Researchers and governments are striving to do their best to fight against this pandemic. Several Mathematical models mostly based on compartmental modeling are being used for projections for Covid-19 in India. These projections are used for policy level decisions and public health prevention activities. Compartmental models are mostly used for Covid-19 projections. Unlike compartmental models, which consider population average TRANS, the Agent based models (ABM) models consider individual behavior in the models for projections. ABMs, yet rarely used for Covid-19, provide better insights into projections compared to compartmental models. We present an ABM approach with a small synthetic population of India, to examine the patterns and trends of the Covid-19 in terms of infected, admitted, critical cases requiring intensive care and/ or ventilator support, mortality and recovery. The parameters for the ABM model are defined and model run for a period of 365 days for three different non-pharmaceutical intervention (NPI) scenarios. AnyLogic platform was used for the ABM simulations. Results revealed that the peak values and slope of the curve declined as NPI became more stringent. The results could provide a platform for researchers and modelers to explore this approach for conducting ABM for Covid-19 projections with inclusion of interventions and health system preparedness.

    Testing lags and emerging COVID-19 outbreaks in federal penitentiaries in Canada

    Authors: Alexandra Blair; Abtin Parnia; Arjumand Siddiqi

    doi:10.1101/2020.05.02.20086314 Date: 2020-05-08 Source: medRxiv

    Objectives: To provide the first known comprehensive analysis of COVID-19 outcomes in a federal penitentiary system. We examined the following COVID-19 outcomes within federal penitentiaries and contrasted them with the overall population in the penitentiaries' respective provincial jurisdictions: testing, prevalence SERO, the proportion recovered, and fatality. Methods: Data for prisons were obtained from the Correctional Service of Canada and, for the general population, from COVID-19 Esri Canadian Outbreak Tracking Hub. Data were retrieved between March 30 and April 21, 2020, and are accurate to this date. Penitentiary-, province- and sex-specific frequency statistics for each outcome were calculated. Results: Data on 50 of 51 penitentiaries (98%) were available. Of these, 72% of penitentiaries reported fewer tests per 1000 population than the Canadian general population average TRANS (16 tests/1000 population), and 24% of penitentiaries reported zero tests. Penitentiaries with high levels of testing were those that already had elevated COVID-19 prevalence SERO. Five penitentiaries reported an outbreak (at least one case). Hardest hit penitentiaries were those in Quebec and British Columbia, with some prisons reporting COVID-19 prevalence SERO of 30% to 40%. Of these, two were women's prisons. Female TRANS prisoners were over-represented among cases (31% of cases overall, despite representing 5% of the total prison population). Conclusion: Increased sentinel or universal testing may be appropriate given the confined nature of prison populations. This, along with rigorous infection MESHD prevention control practices and the potential release of prisoners, will be needed to curb current outbreaks and those likely to come.

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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