engleski

Investigating the common features of COVID-19 highly infected countries using k-means cluster analysis

One could ask what are the common features of COVID-19 highly infected countries. Various researchers have tackled this research problem taking into account a range of different variables. This paper investigates the properties of COVID-19 highly infected countries using k-means cluster analysis. The optimum number of clusters was identified using the two-step cluster procedure according to the Schwarz's Bayesian Information Criterion (BIC) with the log-likelihood distance measure. Results of the analysis have shown that developed, tourism-oriented countries with elderly population structure, improved human rights and index of freedom are more vulnerable to the risk of COVID-19 contagion. Limitation of the paper is related to the fact that data for COVID-19 common features were not available for the year 2020 but have been provided for the most recent year, mostly 2018. The results obtained from this analysis have important policy implications for economic and health care policy makers.

COVID-19 ; K-means cluster analysis ; International tourism

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