Combining rank-size and k-means for clustering countries over the COVID-19 new deaths per million

https://doi.org/10.1016/j.chaos.2022.111975Get rights and content
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Highlights

  • Analysis of the COVID-19 new deaths per million in 35 countries

  • Rank-size modelling via a third-degree polynomial relationship

  • Clustering applied to country-specific parameters resulting from the rank-size model

  • Study of meaningfulness of the identified clusters of countries

Abstract

This paper deals with the cluster analysis of selected countries based on COVID-19 new deaths per million data. We implement a statistical procedure that combines a rank-size exploration and a k-means approach for clustering. Specifically, we first carry out a best-fit exercise on a suitable polynomial rank-size law at an individual country level; then, we cluster the considered countries by adopting a k-means clustering procedure based on the calibrated best-fit parameters. The investigated countries are selected considering those with a high value for the Healthcare Access and Quality Index to make a consistent analysis and reduce biases from the data collection phase. Interesting results emerge from the meaningful interpretation of the parameters of the best-fit curves; in particular, we show some relevant properties of the considered countries when dealing with the days with the highest number of new daily deaths per million and waves. Moreover, the exploration of the obtained clusters allows explaining some common countries' features.

Keywords

COVID-19
K-means clustering
Rank-size analysis

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