{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T05:49:44Z","timestamp":1793857784677,"version":"4.6.1"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:00:00Z","timestamp":1761523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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\u2019s highly heterogeneous burden highlights the need for municipal-scale geospatial analyses to identify actionable hotspots for targeted interventions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u20133 monthly lags.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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, Paran\u00e1, 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2025.1620914","type":"journal-article","created":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T06:21:48Z","timestamp":1761546108000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Geospatial clustering reveals dengue hotspots across Brazilian municipalities, 2024"],"prefix":"10.3389","volume":"13","author":[{"given":"Brena F.","family":"Sena","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bobby Brooke","family":"Herrera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danyelly Bruneska Gondim","family":"Martins","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose Luiz","family":"Lima Filho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/s0966-842x(01)02288-0","article-title":"Epidemic dengue\/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century","volume":"10","author":"Gubler","year":"2002","journal-title":"Trends Microbiol"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1590\/S0102-311X2013000100002","article-title":"Dengue: twenty-five years since reemergence in Brazil","volume":"29","author":"Teixeira","year":"2013","journal-title":"Cad Saude Publica"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"e12","DOI":"10.1590\/s1678-9946202466012","article-title":"A spatial case\u2013control study on symptomatic and inapparent dengue infections in an endemic city in Brazil","volume":"66","author":"Figueiredo","year":"2024","journal-title":"Rev Inst Med Trop Sao Paulo"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1056","DOI":"10.1186\/s12879-024-09813-z","article-title":"Space-time dynamics of the dengue epidemic in Brazil, 2024: an insight for decision making","volume":"24","author":"Souza","year":"2024","journal-title":"BMC Infect Dis"},{"key":"ref5","year":"2025"},{"key":"ref6","author":"Ester","year":"1996"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1145\/3068335","article-title":"DBSCAN revisited, revisited: why and how you should (still) use DBSCAN","volume":"42","author":"Schubert","year":"2017","journal-title":"ACM Trans Database Syst"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.apgeog.2018.02.005","article-title":"Analyzing spatial patterns of dengue cases using DBSCAN clustering in Kaohsiung, Taiwan","volume":"92","author":"Chen","year":"2018","journal-title":"Appl Geogr"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"6397","DOI":"10.3390\/ijerph181264","article-title":"Detecting clusters of infectious diseases using spatial DBSCAN and surveillance data","volume":"18","author":"Zhao","year":"2021","journal-title":"Int J Environ Res Public Health"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"5948","DOI":"10.1038\/s41598-024-58202-8","article-title":"Climate change, thermal anomalies, and the recent progression of dengue in Brazil","volume":"14","author":"Barcellos","year":"2024","journal-title":"Sci Rep"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"88","DOI":"10.3390\/rs15010088","article-title":"Modified space\u2013time DBSCAN for spatiotemporal clustering in infectious-disease monitoring","volume":"15","author":"Ahmed","year":"2023","journal-title":"Remote Sens"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"e0012397","DOI":"10.1371\/journal.pntd.0012397","article-title":"Effects of the El Ni\u00f1o-Southern Oscillation and seasonal weather conditions on Aedes aegypti infestation in the State of S\u00e3o Paulo (Brazil): A Bayesian spatio-temporal study","volume":"18","author":"Pirani","year":"2024","journal-title":"PLoS Negl Trop Dis"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"794","DOI":"10.1016\/j.scitotenv.2018.08.019","article-title":"Correlation of dengue incidence and rainfall occurrence using wavelet transform for Jo\u00e3o Pessoa city","volume":"647","author":"Santos","year":"2019","journal-title":"Sci Total Environ"},{"key":"ref14","year":"2025"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"e0009773","DOI":"10.1371\/journal.pntd.0009773","article-title":"The impact of climate suitability, urbanisation, and connectivity on the expansion of dengue in 21st century Brazil","volume":"54","author":"Lee","year":"2021","journal-title":"PLoS Negl Trop Dis"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"S125","DOI":"10.1590\/s0102-311x2009001300012","article-title":"Irregular water supply, household usage and dengue: a bio-social study in the Brazilian northeast","volume":"25","author":"Caprara","year":"2009","journal-title":"Cad Saude Publica"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"100188","DOI":"10.1016\/j.onehlt.2020.100188","article-title":"Do socioeconomic factors drive Aedes mosquito vectors and their arboviral diseases?","volume":"11","author":"Whiteman","year":"2020","journal-title":"One Health"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"e188","DOI":"10.1017\/S0950268821001882","article-title":"Spatial analysis of the incidence of dengue, zika and chikungunya and socioeconomic determinants in the city of Rio de Janeiro, Brazil","volume":"149","author":"Queiroz","year":"2021","journal-title":"Epidemiol Infect"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"e70","DOI":"10.1016\/S1473-3099(16)30545-X","article-title":"Disease and economic burdens of dengue","volume":"17","author":"Castro","year":"2017","journal-title":"Lancet Infect Dis"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1590\/1413-81232015212.09592015","article-title":"Temporal relationship between rainfall, temperature and dengue in S\u00e3o Lu\u00eds, Maranh\u00e3o","volume":"21","author":"Silva","year":"2016","journal-title":"Ci\u00eanc Sa\u00fade Colet"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"e0012726","DOI":"10.1371\/journal.pntd.0012726","article-title":"Impact of the COVID-19 pandemic on dengue in Brazil: Interrupted time series analysis of changes in surveillance and transmission","volume":"18","author":"Oliveira Roster","year":"2024","journal-title":"PLoS Negl Trop Dis"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1186\/s41182-025-0052-7","article-title":"Assessing dengue forecasting methods: a comparative study of statistical models and machine learning in Rio de Janeiro","volume":"53","author":"Chen","year":"2025","journal-title":"Trop Med Health"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"e209","DOI":"10.1016\/S2542-5196(20)30292-8","article-title":"Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study","volume":"5","author":"Lowe","year":"2021","journal-title":"Lancet Planet Health"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"158","DOI":"10.3390\/v17020158","article-title":"Rising incidence and spatiotemporal dynamics of emerging and reemerging arboviruses in Brazil","volume":"17","author":"Daudt-Lemos","year":"2025","journal-title":"Viruses"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"101042","DOI":"10.1016\/j.lana.2025.101042","article-title":"Leveraging machine learning on hospitalizations in the dynamics of dengue spread in Brazil: an ecological study of health systems resilience","volume":"44","author":"de Castro-Nunes","year":"2025","journal-title":"Lancet Reg Health Am"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"e1760","DOI":"10.1371\/journal.pntd.0001760","article-title":"Refining the global spatial limits of dengue virus transmission by evidence-based consensus","volume":"6","author":"Brady","year":"2012","journal-title":"PLoS Negl Trop Dis"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"1803","DOI":"10.1093\/aje\/kwac090","article-title":"Machine-learning-based forecasting of dengue fever in Brazilian cities using epidemiologic and meteorological variables","volume":"191","author":"Roster","year":"2022","journal-title":"Am J Epidemiol"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"e0220106","DOI":"10.1371\/journal.pone.0220106","article-title":"Forecasting dengue fever in Brazil: an assessment of climate conditions","volume":"14","author":"Stolerman","year":"2019","journal-title":"PLoS One"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"e411","DOI":"10.1371\/journal.pntd.0000411","article-title":"Use of mapping and spatial and space-time modeling approaches in operational control of Aedes aegypti and dengue","volume":"3","author":"Eisen","year":"2009","journal-title":"PLoS Negl Trop Dis"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1186\/s12889-025-0973-0","article-title":"Forecasting dengue across Brazil with LSTM neural networks","volume":"25","author":"Chen","year":"2025","journal-title":"BMC Public Health"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"1456043","DOI":"10.3389\/fpubh.2024.1456043","article-title":"Analysis of the interrelationship between precipitation and confirmed dengue cases in the city of Recife (Brazil) covering climate and public health information","volume":"12","author":"Borges","year":"2024","journal-title":"Front Public Health"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"1508","DOI":"10.1038\/s41564-019-0476-8","article-title":"The current and future global distribution and population at risk of dengue","volume":"4","author":"Messina","year":"2019","journal-title":"Nat Microbiol"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"e89783","DOI":"10.1371\/journal.pone.0089783","article-title":"Estimating past, present, and future trends in the global distribution and abundance of Aedes aegypti mosquitoes","volume":"9","author":"Liu-Helmersson","year":"2014","journal-title":"PLoS One"},{"key":"ref34","year":""},{"key":"ref35","doi-asserted-by":"publisher","first-page":"9","DOI":"10.3390\/ijerph14010009","article-title":"The zika virus epidemic in Brazil: from discovery to future implications","volume":"14","author":"Lowe","year":"2017","journal-title":"Int J Environ Res Public Health"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1038\/nature12060","article-title":"The global distribution and burden of dengue","volume":"496","author":"Bhatt","year":"2013","journal-title":"Nature"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"150066","DOI":"10.1038\/sdata.2015.66","article-title":"The climate hazards infrared precipitation with stations--a new environmental record for monitoring extremes","volume":"2","author":"Funk","year":"2015","journal-title":"Sci Data."},{"key":"ref38","article-title":"Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS)","year":""}],"container-title":["Frontiers in Public Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2025.1620914\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T06:21:50Z","timestamp":1761546110000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2025.1620914\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,27]]},"references-count":38,"alternative-id":["10.3389\/fpubh.2025.1620914"],"URL":"https:\/\/doi.org\/10.3389\/fpubh.2025.1620914","relation":{},"ISSN":["2296-2565"],"issn-type":[{"value":"2296-2565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,27]]},"article-number":"1620914"}}