Epidemic Characteristics, Spatiotemporal Pattern, and Risk Factors of Other Infectious Diarrhea in Fujian Province From 2005 to 2021: Retrospective Analysis

Climatic drivers of infectious diarrheal disease epidemics in China: An empirical dynamic modeling analysis of 21 million cases

Infectious diarrheal diseases continue to impose a heavy public health burden in China, despite significant advancements in sanitation and economic development. While existing evidence has linked climate factors to the dynamics of these diseases, the heterogeneous climatic conditions and complex nonlinear interactions among meteorological variables give rise to intricate epidemic patterns that complicate the identification of causal drivers underlying the observed spatial and temporal variability in disease incidence. To address this gap, we conducted a nationwide study across 365 city-level regions in China from 2005 to 2022. Based on high-resolution surveillance data and meteorological records, we applied an empirical dynamic modeling framework. We inferred causal links between climatic drivers and six notifiable infectious diarrheal diseases using convergent cross-mapping, and further assessed the dynamic impacts of these drivers through multivariate forecast improvement and scenario exploration across different climatic zones. Our results reveal that, except for cholera, infectious diarrheal diseases are predominantly influenced by temperature, relative humidity, and sunshine hours. Temperature generally promotes the incidence of typhoid fever, bacillary dysentery, and other infectious diarrhea, while the influence of relative humidity and sunshine hours varies with environmental context. This study not only characterizes the epidemiological trends of infectious diarrhea over nearly two decades but also demonstrates the feasibility of using EDM to uncover dynamic nonlinear interactions in climate-disease systems. By integrating empirical dynamic modeling into public health frameworks, our approach provides a scalable and effective tool for disentangling complex climate-disease interactions in a warming world, thereby informing more tailored public health interventions in response to climate change.