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 31 determinant , describing a diverse set of socio-economic characteristics, in explaining the coronavirus pandemic outcome  .
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 .
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31 determinant
29 determinants
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. 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