From graph models to intelligent decision-making: a review of spatio-temporal graph neural networks for regional disease risk prediction and etiology mining
Abstract
BACKGROUND: 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.
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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…
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