Impact of meteorological factors on other infectious diarrhea in mainland China: comprehensive risk assessment, forecasting and early warning.

Other infectious diarrhea (OID) is a major public health burden in China and a typical weather-sensitive disease, yet nationwide evidence that links comprehensive risk assessment with forecasting and early warning remains limited. Using surveillance and meteorological data from 316 mainland Chinese cities (2014-2019), we developed an IPCC-based hazard-exposure-vulnerability framework to assess meteorology-related OID risk. A quasi-Poisson distributed lag non-linear model and multivariate meta-analysis were used to quantify temperature-related effects and map spatial patterns of hazard-exposure, sensitivity, adaptability, and vulnerability. High-risk cities were persistently concentrated in Southern, Jiangnan, and Southwest China. Using a unified meteorology-OID dataset, we compared SARIMAX, GAM, and RF forecasting models. RF achieved the best predictive performance (training: R2 = 0.91, RMSE = 39.46, MAE = 23.82; test: R2 = 0.76, RMSE = 41.44, MAE = 31.39), and observed-predicted fits across 10 representative cities yielded R2 > 0.974. An RF-based early warning system with the 75th percentile (P75) of historical weekly cases as the alert threshold performed well (sensitivity: 94.69%, specificity: 86.18%, AUC: 0.918). Overall, integrating meteorology-driven risk assessment with machine-learning forecasting can support earlier alerts, targeted interventions, and climate-resilient control of weather-sensitive infectious diseases.