CASE-FATALITY RATE BY COVID-19: A HIERARCHICAL BAYESIAN ANALYSIS OF COUNTRIES IN DIFFERENT REGIONS OF THE WORLD
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Abstract
The main goal of this study is the statistical analysis of deaths/cases epidemiologically defined as Case-Fatality Rate (CFR) due to novel coronavirus (SARSCoV-2) for 113 countries in different regions of the world in presence of some economic, health and social factors. The considered dataset refers to the accumulated daily counts of reported cases and deaths for a period ranging from the beginning of the COVID-19 pandemics in each country until July 25, 2020 the final follow-up day period. A binomial logistic regression model in presence of a random effect is assumed in the data analysis. The statistical analysis is considered under a hierarchical Bayesian approach using MCMC (Markov Chain Monte Carlo) methods do get the posterior summaries of interest.
The results we found are interesting considering the epidemiological interpretation, which could be of great interest to epidemiologists, health authorities, and the general public in the face of a complex pandemic in all its aspects, like the one we are experiencing.
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