{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T05:18:25Z","timestamp":1787894305981,"version":"build-2784847793"},"reference-count":52,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"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>Objective<\/jats:title>\n                    <jats:p>Meteorological factors influence dengue transmission through nonlinear and lagged effects. This study assessed these associations in Hainan Province and developed an early risk prediction index.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Weekly dengue case and meteorological data from Hainan Province (2018\u20132019 and 2023\u20132024) were analyzed using distributed lag non-linear models (DLNMs). An early risk prediction index(ERPI) was constructed from DLNM-derived cumulative meteorological risk signals and imported cases. Predictive performance was evaluated using leave-one-year-out cross-validation and assessed by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 927 dengue cases reported during the four modelled years, 727 (78.43%) were locally acquired. Weekly mean daily maximum temperature (Tmax), precipitation (Pmean), wind speed (WSmean), and diurnal temperature range (DTRmean) showed nonlinear, lag-dependent associations with dengue risk, with distinct temporal patterns. Higher Tmax showed longer-lag effects, peaking at 33.73\u202f\u00b0C at lag 8\u202fweeks (\n                      <jats:italic>RR<\/jats:italic>\n                      \u202f=\u202f2.45, 95% CI: 1.70\u20133.63). Its cumulative effect was strongest over lags 5\u20138\u202fweeks, peaking at approximately 32\u202f\u00b0C. Pmean showed a nonlinear association, peaking at 4\u202fmm\/day at a lag of 4\u202fweeks (\n                      <jats:italic>RR<\/jats:italic>\n                      \u202f=\u202f1.92, 95% CI: 1.59\u20132.31), with significant cumulative effect across both the 1\u20134- and 5\u20138-week windows. Low WSmean and moderate DTRmean showed their strongest cumulative associations over lags 1\u20134\u202fweeks, peaking at 2.0\u202fm\/s and 6.1\u202f\u00b0C, respectively. These harmful associations attenuated over lags 5\u20138\u202fweeks, whereas higher WSmean and DTRmean were generally protective. The ERPI predicted local dengue occurrence within the subsequent 1\u20134-week windows, with AUCs of 0.762\u20130.843. Across these windows, sensitivities were 0.768\u20130.797, specificities were 0.644\u20130.708, PPVs were 0.468\u20130.624, and NPVs were 0.822\u20130.911.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In Hainan, high temperature was primarily associated with medium-to-long-term dengue risk, whereas low-to-moderate diurnal temperature range increased short-term risk. Precipitation was associated with risk across both short and medium-to-long lags, while cumulative wind-speed associations were strongest over shorter lags and attenuated thereafter. The DLNM-based early risk prediction index may support meteorology-informed early warning and dengue control in tropical settings, although requiring external validation is needed before operational implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1919479","type":"journal-article","created":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T04:31:14Z","timestamp":1787891474000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Meteorological drivers and short-term prediction of dengue fever in a tropical region of China: evidence from Hainan Province"],"prefix":"10.3389","volume":"14","author":[{"given":"Yiyao","family":"Lian","sequence":"first","affiliation":[{"name":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Division of Infectious Disease, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine","place":["Beijing, China"]},{"name":"National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Jin","sequence":"additional","affiliation":[{"name":"Hainan Provincial Center for Disease Control and Prevention (Hainan Academy of Preventive Medicine)","place":["Haikou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufei","family":"Wang","sequence":"additional","affiliation":[{"name":"Office of General Administration, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Qiu","sequence":"additional","affiliation":[{"name":"Hainan Provincial Center for Disease Control and Prevention (Hainan Academy of Preventive Medicine)","place":["Haikou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Chen","sequence":"additional","affiliation":[{"name":"Hainan Provincial Center for Disease Control and Prevention (Hainan Academy of Preventive Medicine)","place":["Haikou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuqing","family":"Guo","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Division of Infectious Disease, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Automation, Tsinghua University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dapeng","family":"Yin","sequence":"additional","affiliation":[{"name":"Hainan Provincial Center for Disease Control and Prevention (Hainan Academy of Preventive Medicine)","place":["Haikou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbiao","family":"Hu","sequence":"additional","affiliation":[{"name":"Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology","place":["Brisbane, QLD, Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Wang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Division of Infectious Disease, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,28]]},"reference":[{"key":"ref1","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":"ref2","year":"2025"},{"key":"ref3","year":"2021"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1007\/s00484-023-02446-y","article-title":"Effects of temperature, relative humidity, and illumination on the entomological parameters of Aedes albopictus: an experimental study","volume":"67","author":"Cai","year":"2023","journal-title":"Int J Biometeorol"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"100452","DOI":"10.1016\/j.onehlt.2022.100452","article-title":"The association between dengue case and climate: a systematic review and 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