Development and Evaluation of a Dengue Meteorological Risk Index (DMRI) in Southern China

Jianxiong Hu; Liling Lin; Honglong Chen; Qimin Fang; Di Wu; Guanhao He; Tao Liu; Fangfang Zeng; Fengrui Jing; Ziqing Lin; Fanna Liu; Xiaofeng Liang; Min Kang; Wenjun Ma

Abstract

Background: Dengue fever remains a critical public health challenge in Southern China, with outbreaks heavily driven by climatic conditions. To facilitate climate-informed early warning, this study aimed to develop and validate a Dengue Meteorological Risk Index (DMRI). Methods: Dengue surveillance records and ERA5-Land meteorological reanalysis data were collected from the six cities with the highest cumulative dengue incidence in Guangdong Province, Southern China. Using the training dataset (2016–2019), Generalized Additive Mixed Models (GAMMs) were employed to quantify the non-linear exposure-response relationships between dengue incidence and key meteorological drivers, including temperature, relative humidity, and temporal dynamics. Subsequently, a Random Forest model was utilized to estimate the relative importance of each variable. The DMRI was then formulated by integrating these relationships and weighted components, stratified into early warning tiers, and externally validated using the 2015 dataset. Results: GAMM analyses revealed an inverted U-shaped association between temperature and dengue risk, while relative humidity exhibited a positive association. Random Forest modeling identified temperature and relative humidity as the primary drivers, slightly outweighing the temporal factor. The formulated DMRI demonstrated a significant positive linear correlation with actual disease risk. External validation confirmed robust discriminative capacity across risk tiers, with weekly case counts in high-risk tiers being significantly elevated compared to low-risk tiers. Conclusions: Based on ecological associations, the developed DMRI serves as an accessible tool that translates complex meteorological and temporal drivers into actionable risk tiers within the study area. Rather than a standalone forecasting tool, it serves as a supplementary indicator that may support health authorities in early warning and inform public prevention efforts.

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