| This paper examines the global impact of COVID-19 by clustering 158 coun-tries based on multiple indicators, including health, demographic, and econom-ic variables. Using the k-means algorithm, the study identifies seven distinct clusters, each representing countries with similar pandemic experience and re-sponses. The research highlights significant disparities among these clusters, such as variations in infection rates, mortality, population age, and GDP per capita. This study found that developing countries had lower infection and death rates, but also lower healthcare capacity, while developed countries ex-hibited higher rates of both infection and mortality, reflecting more compre-hensive testing and reporting. By integrating diverse data sources and applying clustering techniques, the paper contributes to a deeper understanding of the global COVID-19 landscape, offering valuable information for policymakers and public health officials. |
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