Forecasting and Analyzing Influenza Activity in Hebei Province, China, Using a CNN-LSTM Hybrid Model

Abstract

Background Influenza, an acute infectious respiratory disease, presents a significant global health challenge. Accurate prediction of influenza activity is crucial for reducing its impact. Therefore, this study seeks to develop a hybrid Convolution Neural Network - Long Short Term Memory neural network (CNN-LSTM) model to forecast the percentage of influenza-like-illness (ILI) rate in Hebei Province, China. The aim is to provide more precise guidance for influenza prevention and control measures. Methods Using ILI% data from 28 national sentinel hospitals in the Hebei Province, spanning from 2010 to 2022, we employed the Python deep learning framework PyTorch to develop four distinct forecasting prediction models. We assessed each model’s prediction performance using mean absolute error (MAE) and root mean square error (RMSE). Results The Seasonal Auto-Regressive Indagate Moving Average (SARIMA) model had the highest error among the four forecasting models, with a MAE value of 0.8913 and an RMSE value of 1.2098. The CNN-LSTM model had the lowest error, with MAE and RMSE values of 0.0.3987 and 0.5448, respectively. The CNN-LSTM model thus had a significantly better prediction performance compared to the SARIMA model, with a 55.26% decrease in MAE and a 54.97% decrease in RMSE. When compared to the standalone Convolution Neural Network (CNN) and Long Short Term Memory neural network (LSTM) models, the CNN-LSTM model showed performance enhancements of 32.86% for MAE and 28.60% for RMSE over CNN, and of 11.05% for MAE and 13.07% for RMSE over LSTM. Conclusion The hybrid CNN-LSTM model had better prediction performances than the SARIMA, CNN, and LSTM models. This hybrid model could provide more accurate influenza activity projections in the Hebei Province.