Utilizing Geospatial Predictive Modeling to Identify Emerging Health Clusters and Strategize Low-Cost Preventive Interventions for County Health Departments (The Science Post, 2026)
Emerging health clusters—unexpected aggregations of disease incidence—often go undetected until they escalate intopublic health emergencies, driving high reactive costs for county health departments. This study addresses the gap inproactive, data-driven surveillance by investigating the utility of geospatial predictive modeling for early clusteridentification and low-cost preventive planning. The primary purpose is to develop and validate a hybrid geospatialframework combining spatial autocorrelation (Getis-Ord Gi*) and machine learning (random forest regression) topredict high-risk health clusters for chronic and communicable diseases. Using a retrospective ecological design, thestudy analyzes five years of de-identified electronic health records and environmental data from three mid-sized U.S.county health departments. Key findings indicate that the hybrid model achieves 87.4% predictive accuracy foremerging clusters up to four weeks in advance, at a marginal cost per county of $0.18 per capita when integrated intoexisting geographic information systems (GIS). Furthermore, the model enables targeted interventions—mobileclinics and targeted outreach—that reduce potential outbreak costs by 34%. The conclusion supports that countyhealth departments can operationalize geospatial predictive modeling using existing data infrastructures to shift fromreactive to preventive public health, substantially reducing both health disparities and long-term expenditures.