tetano
Editor, Senior Moderator
BMC Public Health
. 2024 Aug 12;24(1):2171.
doi: 10.1186/s12889-024-19590-8. Forecasting and analyzing influenza activity in Hebei Province, China, using a CNN-LSTM hybrid model
Guofan Li[SUP] #[/SUP][SUP] 1 [/SUP], Yan Li[SUP] #[/SUP][SUP] 2 [/SUP], Guangyue Han[SUP] 2 [/SUP], Caixiao Jiang[SUP] 2 [/SUP], Minghao Geng[SUP] 2 [/SUP], Nana Guo[SUP] 2 [/SUP], Wentao Wu[SUP] 2 [/SUP], Shangze Liu[SUP] 2 [/SUP], Zhihuai Xing[SUP] 2 [/SUP], Xu Han[SUP] 2 [/SUP], Qi Li[SUP] 3 4 [/SUP]
Affiliations
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 the CNN-LSTM model. Additionally, we utilized R and Python to develop four other models commonly used for predicting infectious diseases. After constructing the models, we employed these models to make retrospective predictions, and compared each model's prediction performance using mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and other evaluation metrics.
Results: Based on historical ILI% data from 28 national sentinel hospitals in Hebei Province, the Seasonal Auto-Regressive Indagate Moving Average (SARIMA), Extreme Gradient Boosting (XGBoost), Convolution Neural Network (CNN), Long Short Term Memory neural network (LSTM) models were constructed. On the testing set, all models effectively predicted the ILI% trends. Subsequently, these models were used to forecast over different time spans. Across various forecasting periods, the CNN-LSTM model demonstrated the best predictive performance, followed by the XGBoost model, LSTM model, CNN model, and SARIMA model, which exhibited the least favorable performance.
Conclusion: The hybrid CNN-LSTM model had better prediction performances than the SARIMA model, CNN model, LSTM model, and XGBoost model. This hybrid model could provide more accurate influenza activity projections in the Hebei Province.
Keywords: Deep Learning; Forecast; Hybrid Model; Influenza; Modeling.
. 2024 Aug 12;24(1):2171.
doi: 10.1186/s12889-024-19590-8. Forecasting and analyzing influenza activity in Hebei Province, China, using a CNN-LSTM hybrid model
Guofan Li[SUP] #[/SUP][SUP] 1 [/SUP], Yan Li[SUP] #[/SUP][SUP] 2 [/SUP], Guangyue Han[SUP] 2 [/SUP], Caixiao Jiang[SUP] 2 [/SUP], Minghao Geng[SUP] 2 [/SUP], Nana Guo[SUP] 2 [/SUP], Wentao Wu[SUP] 2 [/SUP], Shangze Liu[SUP] 2 [/SUP], Zhihuai Xing[SUP] 2 [/SUP], Xu Han[SUP] 2 [/SUP], Qi Li[SUP] 3 4 [/SUP]
Affiliations
- PMID: 39135162
- DOI: 10.1186/s12889-024-19590-8
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 the CNN-LSTM model. Additionally, we utilized R and Python to develop four other models commonly used for predicting infectious diseases. After constructing the models, we employed these models to make retrospective predictions, and compared each model's prediction performance using mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and other evaluation metrics.
Results: Based on historical ILI% data from 28 national sentinel hospitals in Hebei Province, the Seasonal Auto-Regressive Indagate Moving Average (SARIMA), Extreme Gradient Boosting (XGBoost), Convolution Neural Network (CNN), Long Short Term Memory neural network (LSTM) models were constructed. On the testing set, all models effectively predicted the ILI% trends. Subsequently, these models were used to forecast over different time spans. Across various forecasting periods, the CNN-LSTM model demonstrated the best predictive performance, followed by the XGBoost model, LSTM model, CNN model, and SARIMA model, which exhibited the least favorable performance.
Conclusion: The hybrid CNN-LSTM model had better prediction performances than the SARIMA model, CNN model, LSTM model, and XGBoost model. This hybrid model could provide more accurate influenza activity projections in the Hebei Province.
Keywords: Deep Learning; Forecast; Hybrid Model; Influenza; Modeling.