Modelling the impacts of meteorological factors on herpangina: a Bayesian spatiotemporal analysis

BACKGROUND: Herpangina is a common enteroviral illness in young children with recurrent seasonal surges. Although meteorological conditions influence transmission, evidence capturing non-linear and delayed effects across space remains limited. We aimed to quantify the exposure-lag-response associations of temperature, precipitation, and relative humidity with herpangina risk in Japan using a Bayesian spatiotemporal model. METHODS: Weekly sentinel surveillance herpangina cases and meteorological variables were obtained for the 47 prefectures from January 2001 to December 2024. We applied a Bayesian hierarchical spatiotemporal model with distributed lag non-linear functions to estimate the associations between meteorological variables and herpangina risk (RR with 95% credible intervals, CrIs). RESULTS: The analysis included 2,556,514 herpangina cases. Compared with the overall mean, weekly temperature and relative humidity showed inverted U-shaped associations, with peak risks at approximately 23 °C (RR = 1.57; 95%CrI 1.40-1.75) and 87% (RR = 1.50; 95% CrI 1.40-1.60), respectively. Compared with the minimum-risk level (44 mm), weekly total precipitation exhibited a U-shaped association, with increased risks at both low (2 mm; RR = 1.11; 95% CrI 1.03-1.19) and high levels (106 mm; RR = 1.08; 95% CrI 1.02-1.14). CONCLUSIONS: Meteorological conditions were associated with distinct non-linear and lagged effects on herpangina risk. The estimated exposure-lag-response functions provide empirically grounded risk ranges and lead times that can be integrated into routine surveillance to support timely risk communication, paediatric healthcare planning, and targeted prevention in childcare and school settings.