Comparison of K-Means and Two-Step Cluster Analysis Methods for Clustering COVID-19 Data

This study compares the K-Means and two-step cluster analysis methods for clustering COVID-19 data. The dataset had 1,893,941 cumulative cases from January 2020 to October 2021. K-means resulted in eight clusters, while three grouped by nationality, occupation, patient type, risk group. These clusters were categorized based on age, gender, region of infection. Group 1 5,883 workers infected community settings, 2 7,420 foreign migrant industrial settings or through direct contact with patients, 3 6,870 indirect transmission. recommends targeted interventions continued monitoring evaluation clusters. findings can help improve government policies, medical facilities, treatment