A statistical analysis of COVID-19 pandemic based on the temporal evolution of entropy in different countries

Abstract

Objective: Currently the Covid-19 pandemic is studied with great expectations by several epidemiological models with the aim of predicting the future behaviour of the pandemic. Determining the level of disorder in the pandemic can give us insight into the societal reactions to the pandemic the socio-economic structures and health systems in different countries. Methods: We perform a statistical analysis of Covid-19 pandemic using an entropy measure. For this, the Boltzmann-Gibbs-Shannon (BGS) entropy method is applied to the daily case data and the predictability in the covid-19 pandemic is discussed based on its entropic behaviour. The BGS entropy of the time evolution of daily cases in weekly groups from the beginning of the pandemic to 29 August 2021 in the UK, Germany, France, Italy, and Spain, Turkey, Russia and Iran are calculated and the given countries are classified by the predictability of the spread of the pandemic. Results: There is a clear difference in the predictability of the pandemic between the European countries and Turkey, Russia, and Iran. It is also observed that the vaccination programs and the Covid-19 variants of concerns; 20I/501Y.V1, 20H/501.V2, 21A/S:478K and 20J/501Y.V3 have effected the predictability of the pandemic in given countries are observed. Conclusion: The BGS entropy-based approach to determine the disorder in the time evolution of daily cases of the Covid-19 pandemic is effective and the results can be beneficial for comparison of the country classifications generated by the epidemiological models of this pandemic system.