Identifying Similar Groups of Countries According to the Impact of Corona Virus (COVID-19) by a Two-Layer Clustering Method

Abstract

This paper presents a new clustering algorithm to identify groups of countries. First, a layer of several clustering methods is applied to the original dataset. Then, after performing dimensionality reduction techniques like t-SNE or SOM on the resulting data, a second clustering layer (K-Means) is applied to identify the final clusters. This method is applied to a dataset from 163 countries, considering the following variables population, area, Gross Domestic Product (GDP), Gross Domestic Product adjusted for Purchase Power Parity (GDP-PPP), and COVID-19 related data (Confirmed, Recovered, and Deaths). The implementation with SOM dimensionality reduction outperformed the one with t-SNE for the considered dataset. We expect that using this information, countries can have an insight on which measures against COVID-19 replicate or avoid, based on the results in countries from the same cluster.