# Geospatial clustering reveals dengue hotspots across Brazilian municipalities, 2024

## TL;DR

This study uses geospatial analysis to identify dengue hotspots in Brazil, helping to guide targeted public health interventions.

## Contribution

The study introduces a reproducible DBSCAN-based method integrating spatial, temporal, and climatic data to identify dengue hotspots at the municipal level.

## Key findings

- DBSCAN identified 25 high-burden dengue clusters across Brazil, with most municipalities clustered into high-risk areas.
- High case rates were observed in states like Minas Gerais, Paraná, and Bahia, with some isolated municipalities showing high incidence.
- Precipitation was positively associated with dengue incidence at a two-month lag, supporting climate-informed outbreak response.

## Abstract

Dengue virus (DENV) remains a major and recurrent public health challenge in Brazil. In 2024, the country experienced its largest recorded epidemic, with more than six million probable cases and substantial pressure on hospital systems. The epidemic’s highly heterogeneous burden highlights the need for municipal-scale geospatial analyses to identify actionable hotspots for targeted interventions.

We conducted a nationwide clustering analysis using dengue case notifications and hospitalizations from the national SINAN surveillance system, with denominator populations from the Brazilian Institute of Geography and Statistics (IBGE). We calculated standardized case and hospitalization rates per 100,000 population for all municipalities. A multivariate density-based spatial clustering algorithm (DBSCAN) integrated municipality centroids with epidemiologic burden. Parameters (eps, minPts) were selected using k-distance inspection and sensitivity analyses. Temporal stability was assessed through monthly DBSCAN runs using a common parameter set, and climatic associations were evaluated by pairing dengue indicators with CHIRPS precipitation at 0–3 monthly lags.

DBSCAN identified 25 high-burden municipal clusters, with 5,111 municipalities (92.6%) clustered and 408 (7.4%) were classified as noise. Several clusters exhibited average case rates exceeding 20,000 per 100,000 population, particularly in Minas Gerais, Paraná, and Bahia. Some high-incidence municipalities remained geographically isolated and unclustered. Hospitalization-only clustering produced similar geographic patterns. Monthly analyses revealed persistent high-burden clusters, and precipitation was positively associated with incidence at an approximately two-month lag.

This study demonstrates that integrating spatial, temporal, and climatic dimensions into a DBSCAN framework provides a reproducible method for delineating dengue hotspots at the municipal scale. By distinguising high-intensity clusters from low-burden areas, the approach offers and operationally relevant tool for guiding vector control and outbreak response during dengue epidemics in Brazil.