K-means Clustering in Identifying Epidemiological Patterns of Measles in United States of America Between 2009-2024
DOI:
https://doi.org/10.48165/gjs.2026.3109Keywords:
Epidemiological, surveillance, K-means clustering Measles, Public health analytics, Vaccination coverage United StatesAbstract
Tracking measles vaccination coverage is an essential factor in preventing subsequent outbreaks and building herd immunity. Thus, recognizing trends in immunization and surveillance assist in tailoring public health efforts in different locations. The study employs K-means clustering to detect distinctive epidemiological trends among U.S.A. states using measles, mumps, and rubella (MMR) vaccine coverage data obtained across states from kindergarteners from 2009 to 2024 using the CDC dataset. States sourced dataset on population size, percentage surveyed, and coverage of vaccination groups were preprocessed and standardized to exclude outliers. Using the elbow approach and silhouette analysis, we calculated the ideal number of clusters. K-means clustering was then utilized to categorize states based on similarities in surveillance features. In our results, three major clusters appeared: Cluster 1 featured states with substantial populations and wide variation in survey coverage. Cluster 2 included smaller states with regular high survey rates and MMR coverage. Cluster 3 showed modest population sizes and coverage rates. Visualizations indicated major differences in survey approach and immunization criteria across clusters. K means clustering successfully identified hidden patterns in MMR vaccine monitoring trends across USA. These results provide useful insights for modifying public health efforts and enhancing vaccination reporting systems across the country.
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