A Fine-Grained Infectious Disease Prediction Model Driven by Multi-Source Data
Objective To develop a fine-grained infectious disease prediction model based on multi-source data,providing a basis for precise prevention and control of infectious diseases.Methods Based on historical confirmed case data of infectious diseases and external data from medical institutions and society,we propose a Multi-source Multi-grained Spatio-temporal Network(MMSTNet).It fully integrates data of different spatial granularity,leverages graph attention networks to capture spatial correlations,and gated recurrent units to capture temporal correlations,and predicts the number of fine-grained confirmed cases of infectious diseases in the future.Results The prediction error of MMSTNet is smaller than all baselines over all prediction days,with its mean absolute error reduced by 14.4%compared to the best baseline.Conclusion Integrating external data from medical institutions and society,and considering spatial correlations between regions,can effectively improve the accuracy of fine-grained infectious disease predictions.
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