{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T06:47:22Z","timestamp":1776840442544,"version":"3.51.2"},"reference-count":76,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"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>Background<\/jats:title>\n                    <jats:p>Spatiotemporal epidemiology of dengue remains poorly understood in the Philippines and there is scarcity of a nationwide spatiotemporal cluster analysis. This study utilizes long-term nationwide data to identify the spatial patterns and spatiotemporal clustering of dengue incidence in the Philippines.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We obtained monthly data from January 2017 to December 2024 across all provinces from the Philippine Epidemiology Bureau. The data were analyzed via spatial analysis techniques, specifically Moran\u2019s I and local Getis-Ord Gi* to determine spatial autocorrelation and hotspots. Furthermore, Poisson and space\u2013time permutation (STP) models with varying maximum reported cluster size (MRCS) settings were applied to identify dengue spatiotemporal clusters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 1,903,425 dengue cases were reported in the study period, with a high concentration of cases consistently observed in the National Capital Region (NCR). Significant positive spatial autocorrelation was observed in the study period with hotspots varying across the years. Ifugao, Kalinga, Abra, Isabela and Mountain Province are the provinces most frequently identified as hotspots. Areas within the Western Visayas region were consistently identified under the primary clusters by the spatiotemporal models signifying the impact of the 2019 epidemic in the region. Compared with the Poisson models, the STP model had identified more clusters with smaller radii.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>To our knowledge, this is the first spatiotemporal cluster analysis in the Philippines on reported dengue cases at the national scale using spatial scan statistics. The study demonstrated the application of varying MRCS which has effectively detected meaningful clusters. These findings offer health agencies and authorities in the Philippines approaches to further understand disease epidemiology, particularly in terms of spatial and spatiotemporal clustering, which consequently enables the implementation of targeted interventions and resource allocation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1781800","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T05:49:32Z","timestamp":1776836972000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Spatiotemporal clusters and dengue hotspots in the Philippines: a nationwide analysis spanning 2017\u20132024"],"prefix":"10.3389","volume":"14","author":[{"given":"Kenny Oriel A.","family":"Olana","sequence":"first","affiliation":[{"name":"Doctor of Public Health Program, Faculty of Public Health, Chiang Mai University","place":["Chiang Mai, Thailand"]},{"name":"Department of 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