ArticleBMC medical informatics and decision making2026
Research on influenza surveillance and a prediction model based on multi-source data.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveTo develop and validate a multivariate Long Short-Term Memory (LSTM) model that integrates multi-source surveillance data for forecasting influenza activity. This study aimed to identify the most predictive variables and establish an optimized data fusion framework to enhance public health surveillance.
methodsWe collected influenza case data, influenza-like illness (ILI) reports, and symptom monitoring data, along with corresponding meteorological data and Baidu Index data in Baoshan city from January 2022 to June 2025. Spearman correlation analysis was used to verify the relationship between each dataset and influenza case numbers. Furthermore, the SHapley Additive exPlanations (SHAP) was employed to quantify feature importance. A LSTM model was constructed for predictive research, to identify in the optimal multi-source dataset. The prediction model based on this optimal dataset utilized the moving percentile method to determine the best early warning threshold.
resultsInfluenza activity in Baoshan City exhibited distinct seasonality, with outbreaks peaking in winter and spring. ILI reports demonstrated the strongest correlation with confirmed cases (r
conclusionsThis study demonstrates that a strategically simplified LSTM model, leveraging refined multi-source data, can achieve high accuracy and robustness, providing solutions for public health surveillance scenarios. The threshold value of influenza epidemic warning in Baoshan city demonstrates reasonable sensitivity and specificity, and can be recommended as an early warning index of the influenza epidemic in Baoshan city. CLINICAL TRIAL NUMBER: Not applicable.
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