Integrating machine learning and data-driven modelling: predicting dengue outbreaks and revealing spatially heterogeneous climatic drivers in Brazil

Abstract Dengue fever poses a major global health threat, with Brazil experiencing severe recurrent outbreaks driven by climatic, socio-economic and mobility factors. Accurate prediction remains challenging owing to dynamically shifting transmission patterns under intervention. This study employs a physics-informed neural network framework to integrate surveillance data with mechanistic models, inferring time-varying intervention parameters. The model demonstrates a strong capacity to fit state-level data from Brazil. Furthermore, it is used to generate 12-week forecasts for future dengue cases. Given the critical influence of climate on transmission dynamics, we also constructed separate eXtreme Gradient Boosting (XGBoost) models for individual states using epidemiological and meteorological data. SHapley Additive exPlanation (SHAP) analysis was applied to identify key climatic drivers and reveal significant regional heterogeneity. Our findings reveal that the drivers of the outbreak exhibit regional heterogeneity, and this geographical variation shows a strong correlation with the intensity of human activities and regional economic development levels. These insights into spatial heterogeneity and predictive modelling can inform the design of more effective, regionally tailored public health strategies for dengue control in Brazil.