Correlates of the country differences in the infection and mortality rates during the first wave of the COVID-19 pandemic: Evidence from Bayesian model averaging

TL;DR

This study uses Bayesian model averaging to identify key social and economic factors that correlate with COVID-19 infection and mortality rates during the pandemic's first wave, highlighting variability across countries.

Contribution

It introduces a Bayesian model averaging approach to analyze 28 socio-economic and health variables as potential correlates of COVID-19 outcomes across countries.

Findings

Few variables robustly correlate with infection and mortality rates.

The relationship between variables and outcomes varies across countries.

A Jointness Space reveals heterogeneous explanatory power of variables.

Abstract

In the initial wave of the COVID-19 pandemic we observed great discrepancies in both infection and mortality rates between countries. Besides the biological and epidemiological factors, a multitude of social and economic criteria also influence the extent to which these discrepancies appear. Consequently, there is an active debate regarding the critical socio-economic and health factors that correlate with the infection and mortality rates outcome of the pandemic. Here, we leverage Bayesian model averaging techniques and country level data to investigate the potential of 28 variables, describing a diverse set of health and socio-economic characteristics, in being correlates of the final number of infections and deaths during the first wave of the coronavirus pandemic. We show that only few variables are able to robustly correlate with these outcomes. To understand the relationship between the potential correlates in explaining the infection and death rates, we create a Jointness Space. Using this space, we conclude that the extent to which each variable is able to provide a credible explanation for the COVID-19 infections/mortality outcome varies between countries because of their heterogeneous features.