  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.
