Modeling of COVID-19 Pandemic vis-à-vis Some Socio-Economic Factors

Abstract

The impact of the COVID-19 epidemic on the socio-economic status of countries around the world should not be underestimated, when we consider the role it has played in various countries. Many people were unemployed, many households were careful about their spending, and a greater social divide in the population emerged in 14 different countries from the Organization for Economic Co-operation and Development (OECD) and from Africa (that is, in developed and developing countries) for which we have considered the epidemiological data on the spread of infection during the first and second waves, as well as their socio-economic data. We established a mathematical relationship between Theil and Gini indices, then we investigated the relationship between epidemiological data and socio-economic determinants, using several machine learning and deep learning methods. High correlations were observed between some of the socio-economic and epidemiological parameters and we predicted three of the socio-economic variables in order to validate our results. These results show a clear difference between the first and the second wave of the pandemic, confirming the impact of the real dynamics of the epidemic’s spread in several countries and the means by which it was mitigated.