Meteorological drivers and short-term prediction of dengue fever in a tropical region of China: evidence from Hainan Province - Yiyao Lian; Yan Jin; Yufei Wang; Li Qiu; Jingjing Chen; Yuqing Guo; Rui Jiang; Dapeng Yin; Wenbiao Hu; Liping Wang (Frontiers in Public Health, 2026)
Objective 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. Methods Weekly dengue case and meteorological data from Hainan Province (2018–2019 and 2023–2024) 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). Results 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 °C at lag 8 weeks ( RR = 2.45, 95% CI: 1.70–3.63). Its cumulative effect was strongest over lags 5–8 weeks, peaking at approximately 32 °C. Pmean showed a nonlinear association, peaking at 4 mm/day at a lag of 4 weeks ( RR = 1.92, 95% CI: 1.59–2.31), with significant cumulative effect across both the 1–4- and 5–8-week windows. Low WSmean and moderate DTRmean showed their strongest cumulative associations over lags 1–4 weeks, peaking at 2.0 m/s and 6.1 °C, respectively. These harmful associations attenuated over lags 5–8 weeks, whereas higher WSmean and DTRmean were generally protective. The ERPI predicted local dengue occurrence within the subsequent 1–4-week windows, with AUCs of 0.762–0.843. Across these windows, sensitivities were 0.768–0.797, specificities were 0.644–0.708, PPVs were 0.468–0.624, and NPVs were 0.822–0.911. Conclusion 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.