An Anonymized Geospatial Dengue Surveillance Dataset for Risk Stratification: A Municipality–Year Analytical Resource for Unsupervised Clustering

Dengue fever poses an ongoing challenge to global and Colombian public health. Although surveillance microdata are widely available, there remains a gap in converting them into actionable epidemiological intelligence. This study presents a reproducible dataset and analytical resource for transforming routine records into a spatiotemporal framework for territorial risk stratification. To this end, 15 years (2010–2024) of anonymized records from the SIVIGILA in Colombia’s Caribbean region were consolidated, covering 303,801 cases across 197 municipalities. The microdata were aggregated into municipality–year analytical units using seven indicators of magnitude, severity, demographics, and surveillance performance. Subsequently, an unsupervised learning model (K-means) was validated using the Elbow and Silhouette methods. The algorithm consistently identified four heterogeneous epidemiological profiles: high-transmission urban settings, dispersed rural risk municipalities, territories with a pediatric predominance, and clusters of high clinical severity with elevated hospitalization and case fatality rates. In conclusion, this dataset and its methodological framework transform static historical information into an operational tool that facilitates strategic surveillance, the development of interactive dashboards, and the territorial prioritization of evidence-based public health interventions.