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 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 29 determinants, describing a diverse set of socio-economic characteristics, in explaining the coronavirus pandemic outcome. We show that the true mathematical model of the coronavirus outcome is constituted only of few determinants, but the extent to which each determinant is able to provide an adequate explanation varies between countries due to their heterogeneous socioeconomic structures . To understand the relationship between the socio-economic determinants in the specification of the true model  we develop the coronavirus determinants Jointness space. In this space, the potential determinants are linked with each other by their ability to jointly explain the coronavirus outcome. As such the Jointness space can be efficiently implemented for developing socio-economic  policies aimed at prevention of future epidemic crises.
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 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 29 determinants, describing a diverse set of socio-economic characteristics, in explaining the coronavirus pandemic outcome. 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. In this space, two determinants are connected with each other if they are able to jointly explain the coronavirus outcome. As constructed, 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.
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mathematical model of
empirical model behind
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an adequate
a credible
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socioeconomic structures
socio-economic characteristics
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socio-economic
potential
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,
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the potential determinants are linked
two determinants are connected
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by their ability
if they are able
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such the Jointness space can be efficiently implemented for developing
constructed, the obtained map acts as a bridge between theoretical investigations and empirical observations, and offers an alternate view for the joint importance of the
A
determinants when used for developing
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prevention of
preventing