Extracting geographical characteristics about COVID-19 evolution worldwide using machine learning

Admitos-Rafael Passadakis, Anastasios Vlachos, Paraskevi K. Tzouveli, Stefanos D. Kollias

Abstract: Since the beginning of 2020, the whole world has been plagued by the coronavirus pandemic. During the last sixteen months, almost every country in the world has faced several epidemic waves. An intriguing question that arises is whether neighboring countries, similar in regard to their socioeconomic status and the restrictions employed to counter the spread of the virus, showcase similarities in their respective number of cases and deaths. To that end, in this paper we form three clusters of similar countries (European and USA, African-Asian and Latin American) and we use their cumulative data as training data for machine learning models (RNN family, TCN and Attention) that predict the respective cases and deaths of 4 fixed neighboring countries, namely Cyprus, Greece, Italy and Spain. The results of the experiments conducted show that these 4 countries accent bigger similarity with the European cluster, as expected. Thus, evidence is provided bolstering the claim that similar neighboring countries exhibit alike behavior regarding the repercussions of the COVID-19.