The magnitude of the coronavirus disease (COVID-19) pandemic has an enormous impact on the social life and the economic activities in almost every country in the world. Besides the biological and epidemiological factors, a multitude of social and economic criteria also govern the extent of the coronavirus disease spread in the population. Consequently, there is an active debate regarding the critical socio-economic determinants that contribute to the impact of the resulting pandemic. In this paper, we contribute towards the resolution of the debate by leveraging Bayesian model averaging techniques and country level data to investigate the potential of 30 determinants, describing a diverse set of socio-economic characteristics, in explaining the outcome of the first wave of the coronavirus pandemic. We show that the true empirical model behind the coronavirus outcome is constituted only of few determinants , but the extent to which each determinant is able to provide a credible explanation varies between countries due to their heterogeneous socio-economic characteristics . To understand the relationship between the potential determinants in the specification of the true model, we develop the coronavirus determinants Jointness space. The obtained map acts as a bridge between theoretical investigations and empirical observations , and offers an alternate view for the joint importance of the socio-economic determinants when used for developing policies aimed at preventing future epidemic crises.
 Besides the biological and epidemiological factors, a multitude of social and economic criteria also govern the extent of the coronavirus disease spread within a population. Consequently, there is an active debate regarding the critical socio-economic determinants that contribute to the impact of the resulting pandemic. Here, we leverage Bayesian model averaging techniques and country level data to investigate the potential of 31 determinants, describing a diverse set of socio-economic characteristics, in explaining the outcome of the first wave of the coronavirus pandemic. We show that the true empirical model behind the coronavirus outcome is constituted only of few determinants . To understand the relationship between the potential determinants in the specification of the true model, we develop the coronavirus determinants Jointness space. The extent to which each determinant is able to provide a credible explanation varies between countries due to their heterogeneous socio-economic characteristics. In this aspect, the obtained Jointness map acts as a bridge between theoretical investigations and empirical observations and offers an alternate view for the joint importance of the socio-economic determinants when used for developing policies aimed at preventing future epidemic crises.
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