Volume 10 • Issue 2 • PP: 09–14 • 2025
Hierarchical Clustering of Global COVID-19 Statistics: Comparative Insights from Pandemic Indicators
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© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Hierarchical clustering is applied to global COVID-19 data from the Worldometer tracker, whose final update was April 13, 2024, to partition countries into clusters based on epidemiological indicators. Total cases, deaths, recoveries, active cases, tests, population, and per-million measures are standardized and analyzed using agglomerative hierar-chical clustering with Ward linkage. The evaluation yields an average Silhouette score of 38.5%, a Davies–Bouldin value of 0.87, and a Calinski–Harabasz value of 77.6. Dendrograms and principal component analysis (PCA) projections identify four clusters, reflecting differences in pandemic impact and response. The analysis finds that high-burden clusters account for almost 45% of global deaths, while low-burden clusters predominate in more than 40% of nations with fewer than 100,000 accumulated cases. The results illustrate how hierarchical clustering can support comparative public-health analysis by providing quantitative estimates of epidemiological patterns across countries.
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