Forecasting Hand-Foot-and-Mouth Disease in Qingdao City Based on CNN-BiLSTM-Attention Model

Abstract

Purpose:A deep learning model CNN-BiLSTM-Attention was proposed, which realized the prediction of the outbreak rule and trend of HFMD in Qingdao, and could provide scientific basis for the relevant departments to formulate preventive measures..Methods:Based on the data of temperature, humidity and HFMD in Qingdao city, a multi-source heterogeneous data set was constructed and a long-term time series prediction model was proposed based on attention mechanism, which can predict the future incidence trend of HFMD. Then, ablation experiments were performed to verify the importance of each module, and comparison experiments with infectious disease dynamic model (SEIIeQR) and statistical model (ARIMA, SARIMA) proved the accuracy of the proposed model..Results:The ablation experiments demonstrated that the CNN module,Attention module,and BiLSTM module could effectively enhance the model's performance.The comparative experiment results revealed the superiority of the deep learning model in predicting HFMD outbreaks in Qingdao City.Conclusion:The multi-source heterogeneous dataset incorporating average temperature and relative humidity data significantly improved the model's accuracy.The CNN-BiLSTM-Attention model outperforms traditional methods in predicting HFMD incidence in Qingdao City,thereby assisting relevant departments in Qingdao to adopt scientific preventive measures against HFMD.