Spatio-temporal graph neural networks improve prediction accuracy and interpretability for regional disease risk modelingNew computer models improve accuracy for predicting regional disease risks

Regional disease risk prediction is a core component of public health early warning systems. Traditional statistical models and machine learning methods have inherent limitations in handling multi-source heterogeneous data fusion, complex spatio-temporal dependency modeling, and interpretable etiology mining, making it difficult to meet the demands of precise and real-time public health decision-making. This review systematically examines the methodological advances, application scenarios, and future directions of spatio-temporal graph neural networks (ST-GNNs) and multi-source data fusion techniques in regional disease risk prediction and etiology mining, aiming to provide a bridging reference that connects cutting-edge technologies with practical applications for public health researchers, policymakers, and data scientists. Following the PRISMA framework, we systematically searched the Web of Science, PubMed, and IEEE Xplore databases for the period 2023–2026, ultimately including 76 core studies. A four-layer methodological framework encompassing graph construction, fusion strategies, spatio-temporal modeling, and interpretable etiology mining was developed. Representative works are reviewed from two dimensions: prediction tasks (single-disease prediction, multi-disease collaborative forecasting, long-term extrapolation) and etiology mining (spatial transmission tracing, temporal pattern attribution, multi-factor interaction analysis). Five major technical challenges are identified: dynamic graph structure modeling, cross-modal heterogeneous fusion, trade-off between prediction and interpretability, out-of-distribution generalization, and privacy-preserving federated learning. These are complemented by implementation constraints from public health practice, including data availability, computational efficiency, and policy coordination. The main contribution of this review is the construction of a unified methodological framework integrating prediction and etiology mining, and a systematic synthesis of the challenges and future pathways at the frontier of technology and practical implementation. ST-GNNs demonstrate significant advantages in improving prediction accuracy and interpretability. Future developments should deeply integrate foundation models and causal inference to build a “prediction–intervention–evaluation” closed-loop system, providing actionable methodological references for building regional disease early warning systems and formulating public health intervention strategies globally, especially in low- and middle-income regions, thereby enhancing public health emergency response capacity and health equity.