Construction and evaluation of influenza prediction models based on the integration of meteorological,internet,and surveillance data using machine learning
Objective:To Explore the role of meteorological factors and internet data in predicting influenza cases in Suzhou City,and construct influenza prediction models for Suzhou City based on the method of machine learning.Methods:We collected meteorological data,influenza surveillance data,and internet influenza keyword search data in Suzhou from January 1,2012,to December 29,2019.Then,we used cross-correlation analysis to test the lagged relationships between the preliminary screened meteorological factors,influenza keywords,and influenza cases within a 4-week time frame.Based on the lagged correlation analysis,researchers further filtered the keywords.Utilizing the determined different types of keywords,influenza-like illness consultation rate and positive rate,we constructed influenza prediction models by using SARIMA,LSTM,and Att-LSTM methods.Finally,we evaluated each model using the Mean Squared Error(MSE),Mean Absolute Error(MAE),and Root Mean Squared Error(RMSE)metrics.Results:The fitting of the SARIMA model was relatively low,whereas the Att-LSTM model showed a high fitting degree,with its MSE,RMSE,and MAE values respectively being 0.055,0.235 and 0.184.Conclusion:The Att-LSTM model,constructed based on meteorological,internet,and surveillance data,can significantly enhance predictive accuracy.The results will provide a scientific basis for more precise influenza prevention and control efforts in Suzhou.
machine learninginfluenzameteorologysearch indexprediction model
维普
万方数据
