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. 2022 May 2;12(1):7099.
doi: 10.1038/s41598-022-10894-6.

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

Viktor Stojkoski  1   2   3 , Zoran Utkovski  4   5 , Petar Jolakoski  4 , Dragan Tevdovski  6 , Ljupcho Kocarev  4   7
Affiliations

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

Viktor Stojkoski et al. Sci Rep. .

Abstract

The COVID-19 pandemic resulted in great discrepancies in both infection and mortality rates between countries. Besides the biological and epidemiological factors, a multitude of social and economic criteria also influenced the extent to which these discrepancies appeared. 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 whether 28 variables, which describe a diverse set of health and socio-economic characteristics, correlate with the final number of infections and deaths during the first wave of the coronavirus pandemic. We show that only a 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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Explained variation in COVID-19 cases due to government response.
Figure 2
Figure 2
BMA results. Bars for the posterior inclusion probability (PIP), posterior mean (Post. Mean) and the posterior standard deviation (Post. Std.) of each potential correlate. The variables are ordered according to their PIP. The Post. Mean is in absolute value. The signs next to the bar of each variable indicate the direction of its impact. The horizontal lines divide the variables into groups according to their PIP. The horizontal axis is on a logarithmic scale. The setup used to estimate the results is described in SI Section S3.
Figure 3
Figure 3
Jointness Space of the COVID-19 correlates. The color of the edge between a pair of correlates is proportional to their Jointness metric. To visualize the network, we use the Force-Layout drawing algorithm.
See this image and copyright information in PMC

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