Modeling COVID-19 pandemic using Bayesian analysis with application to Slovene data.

ABSTRACT: In the paper, we propose a semiparametric framework for modeling the COVID-19 pandemic. The stochastic part of the framework is based on Bayesian inference. The model is informed by the actual COVID-19 data and the current epidemiological findings about the disease. The framework combines many available data sources (number of positive cases, number of patients in hospitals and in intensive care, etc.) to make outputs as accurate as possible and incorporates the times of non-pharmaceutical governmental interventions which were adopted worldwide to slow-down the pandemic. The model estimates the reproduction number of SARS-CoV-2, the number of infected individuals and the number of patients in different disease progression states in time. It can be used for estimating current infection fata

SUBMITTER: Manevski D 

PROVIDER: S-EPMC7482592 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature