Meteorological Determinants of Hepatitis E Dynamics in Jiangsu Province, China: A Pre-COVID-19 Era Study Focusing on Multi-Route Transmission (2005-2018)

Peihua Li^1*Jia Rui²Kangguo Li²Bin Deng³Hongjie Wei²Xi Tan^1*Tianmu Chen^2*

• ¹Shenzhen Hospital, Beijing University of Chinese Medicine, Shenzhen, China
• ²School of Public Health, Xiamen University, Xiamen, Fujian Province, China
• ³Guizhou Centre for Disease Control and Prevention, Guiyang, Guizhou Province, China

Objectives: This study aimed to investigate the impact of meteorological factors on the incidence and multi-route transmission dynamics of hepatitis E virus (HEV) in Jiangsu Province, China, during the pre-COVID-19 era (2005–2018), and to develop predictive models for informing public health interventions. Study Design: A dual-model study integrating the Multi-Host and Multi-Route Transmission Dynamic Model (MHMRTDM) and Generalized Additive Model (GAM) was employed to quantify meteorological impacts on multi-route HEV transmission. Methods: HEV incidence data (2005–2018) and meteorological variables from provincial and national agencies were analyzed. The MHMRTDM quantified transmission rate coefficients (β, βw and βp′). GAMs linked the transmission coefficients and incidence to meteorological factors, validated using 2017–2018 data. Results: The optimal GAM integrated with the MHMRTDM was established (lowest GCV = 1.705×10-21, R2 = 0.980, lowest RMSE = 3.682×10-11, lowest MAE = 2.987×10-11) . Analysis of four dependent variables (incidence, β, βw and βp′) revealed distinct climate-driven patterns: (1) Incidence exhibited dual seasonal peaks linked to atmospheric pressure, sunshine duration, and humidity; (2) Host-to-person transmission (βp′) was most sensitive to climatic conditions, peaking at 1013 hPa and declining sharply above 75% humidity, while susceptible person-to-infected person (β) and environment-to-person (βw) transmission were primarily modulated by humidity and wind speed; (3) The GAM validation confirmed robust performance for transmission coefficients (P <0.001). Predictions for 2019–2021 highlighted persistent seasonal bimodality, reinforcing the model’s utility for outbreak forecasting. Conclusions: Meteorological factors drive HEV transmission through distinct pathways, with host-to-person interactions being particularly climate-sensitive. While the GAM provided valuable insights, future research incorporating behavioral and land-use factors, as well as causal inference models, will be critical for improving the understanding and predictive accuracy of HEV transmission dynamics.