Country Classifications Through a COVID-19 Pandemic Model with Dynamic Parameters and Socioeconomics Indicators

Abstract

COVID-19 is a contagious disease that is spreading rapidly to all countries in the world. Data shows that the progression of the disease is different from one country to another. The dynamic of new cases and death are influenced by rules that restrict human interaction, the quality of healthcare services, and the complexity of human behaviors. This paper aims to show how socio-economic factors contribute to the patterns of the pandemic in various countries. The analysis was done for two periods, reflecting two earlier phases of the pandemic: the first six months and the next six months afterward. The results provide helpful lesson-learned for regional and global policymakers to be better prepared with better policies in case of future pandemics. The analysis is done as follows. Firstly, we classify the countries based on the biweekly series of the infection parameter (????) and the recovery one (γ). Both parameters are estimated from the COVID-19 data by using the SIR (Susceptible, Infectious, Recovered) model. Secondly, we classify the countries based on the ???? and γ parameters or the combinations of socio-economic indicators. The chosen indicators are the Economic Complexity Index (ECI), the Compliance Risk Index (CRI), the Anti-Money Laundering (AML) index, the Gross Domestic Product (GDP) per Capita, and School Enrollment. The indices reflect the economic condition, safeness, education, and obedience to the government. For the first six-month phase of the pandemic, we choose 29 countries, and for the second phase, we choose 34 countries. The chosen countries are the ones that rank highest in terms of COVID-19 testing. Our analysis shows how countries are classified based on the pattern of COVID-19 progression and which indices are the most relevant for each cluster.