Geospatial clustering reveals dengue hotspots across Brazilian municipalities, 2024

Bibliographic Data

ID22075282
AuthorsBrena F Sena, Barbara Sena (0000-0002-8894-5169, Universidade Federal de Pernambuco, corresponding author), Bobby Brooke Herrera (0000-0002-2438-6716, Rutgers, the State University of New Jersey), Danyelly Bruneska Gondim Martins (0000-0002-6005-7864, Universidade Federal de Pernambuco), José Luiz Lima Filho (0000-0003-0897-7775, Universidade Federal de Pernambuco)
Year2025
Volume13
Pages1620914-1620914
Publication date2025-10-27
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2025.1620914
PMID41221228
OpenAlexW4415567132
LanguageEN
References cited33

Introduction: 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. Methods: 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. Results: 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. Discussion: 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

Cluster analysis · DBSCAN · Dengue fever · Geospatial analysis · Outbreak · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Mosquito-borne diseases and control

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