Clustering Countries in the Context of the Pandemic and Underlying Conditions

Abstract

The COVID-19 pandemic has revealed the state of underlying conditions of countries in terms of health system, sanitary infrastructure, governance, among others. This study aims to classify countries using COVID-19-related variables such as the lethality rate, the contagion growth rate, the stringency index, and underlying conditions of countries directly related to COVID-19 such as access to clean water, hospital beds per 10000 inhabitants, government effectiveness index, population older than 65 years old and economic growth rate. To determine the clusters of a set of countries from all continents (29 from Africa, 35 from Asia, 35 from Europe, 11 from North America, 2 from Oceania and 8 from South America), the k-means partitioning method is used. This approach consists in constructing partitions and evaluate their intra-class and inter-class similarity. Based on the results, three clusters are identified: i. Severely affected countries with high stringency and moderate capacity, ii. Moderately affected countries with moderate stringency and high capacity and iii. Severely affected countries with low stringency but low capacity.