Insights Into The Predictability And Similarity Of Covid-19 Worldwide Lethality

This paper performs a systematic investigation into the temporal evolution of daily death cases of COVID-19 worldwide lethality considering 90 countries. We apply the information theory quantifiers, more specifically the Permutation entropy (Hs) and Fisher information measure (Fs) to construct the Shannon-Fisher causality plane (SFCP), which allows us to quantify the disorder and evaluate randomness present in the time series of daily death cases related to COVID-19 in each country. Moreover, we employ Hs and Fs to rank the COVID-19 lethality in these countries based on the complexity hierarchy. Our findings reveal that the countries that are located farther from the random ideal position (Hs = 1, Fs = 0) in the SFCP such as Taiwan, Vietnam, New Zealand, Singapore, Monaco, Iceland, Thailand, Bahamas, Cyprus, Australia, and Norway are characterized by a less entropy and low disorder, which leads to high predictability of the COVID-19 lethality. Otherwise, the countries that are located near to the random ideal position (Hs = 1, Fs = 0) in the SFCP such as Ecuador, Czechia, Iraq, Colombia, Belgium, Italy, Philippines, Iran, Peru, and Japan are characterized by high entropy and high disorder, which implies low predictability of the COVID-19 lethality. We also employ two cluster techniques to analyze the similarity considering the temporal evolution of COVID-19 worldwide lethality for the countries investigated. Based on the values of Hs, Fs and our cluster analysis, we suggest that this health crisis will only be adequately combated through global adherence to scientific exchange and technology sharing to homogenize the actions to combat the COVID-19.