PDF(Pubmed)
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 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.
CONCLUSIONS: 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.
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.
CONCLUSIONS: 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.
摘要：
背景：流感，急性传染性呼吸道疾病,提出了重大的全球卫生挑战。准确预测流感活动对于减少其影响至关重要。因此,本研究旨在开发一种混合卷积神经网络-长短期记忆神经网络（CNN-LSTM）模型，以预测河北省流感样疾病（ILI）发生率的百分比，中国。旨在为流感防控措施提供更精准的指导。
方法：使用来自河北省28家国家哨点医院的ILI%数据，从2010年到2022年，我们使用Python深度学习框架PyTorch来开发CNN-LSTM模型。此外,我们利用R和Python开发了其他四种常用于预测传染病的模型。在构建模型之后，我们使用这些模型来进行回顾性预测，并使用平均绝对误差(MAE)比较了每个模型的预测性能，均方根误差(RMSE)，平均绝对百分比误差(MAPE)，和其他评估指标。
结果：根据河北省28家国家级哨点医院的历史ILI%数据，季节性自回归指数移动平均线(SARIMA)，极端梯度提升（XGBoost），卷积神经网络(CNN)构建长短期记忆神经网络(LSTM)模型。在测试集上，所有模型都有效地预测了ILI%趋势。随后，这些模型被用来预测不同的时间跨度。在各个预测期间，CNN-LSTM模型表现出最佳的预测性能，其次是XGBoost模型，LSTM模型,CNN模型，和SARIMA模型,表现出最差的表现。
结论：混合CNN-LSTM模型比SARIMA模型具有更好的预测性能，CNN模型，LSTM模型,和XGBoost模型。这种混合模型可以提供更准确的河北省流感活动预测。
方法：使用来自河北省28家国家哨点医院的ILI%数据，从2010年到2022年，我们使用Python深度学习框架PyTorch来开发CNN-LSTM模型。此外,我们利用R和Python开发了其他四种常用于预测传染病的模型。在构建模型之后，我们使用这些模型来进行回顾性预测，并使用平均绝对误差(MAE)比较了每个模型的预测性能，均方根误差(RMSE)，平均绝对百分比误差(MAPE)，和其他评估指标。
结果：根据河北省28家国家级哨点医院的历史ILI%数据，季节性自回归指数移动平均线(SARIMA)，极端梯度提升（XGBoost），卷积神经网络(CNN)构建长短期记忆神经网络(LSTM)模型。在测试集上，所有模型都有效地预测了ILI%趋势。随后，这些模型被用来预测不同的时间跨度。在各个预测期间，CNN-LSTM模型表现出最佳的预测性能，其次是XGBoost模型，LSTM模型,CNN模型，和SARIMA模型,表现出最差的表现。
结论：混合CNN-LSTM模型比SARIMA模型具有更好的预测性能，CNN模型，LSTM模型,和XGBoost模型。这种混合模型可以提供更准确的河北省流感活动预测。
