■流感是一种急性呼吸道传染病,具有重大的全球疾病负担。此外,冠状病毒病2019大流行及其相关的非药物干预措施(NPI)给流感的传播带来了不确定性。然而,关于用于流感预测的创新模型和方法的性能的比较研究有限。因此,本研究旨在基于哨点监测数据,使用三种方法预测中国具有不同气候特征的地区流感样疾病(ILI)的趋势,并评估和比较其预测性能.
■广义可加模型(GAM),基于Gate递归单元(GRU)的深度学习混合模型,建立了自回归移动平均-广义自回归条件异方差(ARMA-GARCH)模型来预测ILI1-,2-,3-,在北京提前4周,天津,山西,湖北,重庆,广东,海南,和中国的香港特别行政区,基于2011年至2019年的哨点监测数据。三个相关指标,即,平均绝对百分比误差(MAPE),均方根误差(RMSE),和R的平方,进行了计算,以评估和比较三个模型的拟合优度和稳健性。
■考虑到地图,RMSE,和R平方值,ARMA-GARCH模型表现最好,而基于GRU的深度学习混合模型表现出中等性能,而GAM在中国的八种设置中做出的预测精度最低。此外,随着未来几周的增加,模型的预测性能下降。此外,阻断交叉验证表明,所有模型对数据变化均具有稳健性,且过拟合风险较低.
■我们的研究表明,与基于GAM和GRU的深度学习混合模型相比,ARMA-GARCH模型在预测中国ILI趋势方面表现出最佳准确性。因此,在未来,ARMA-GARCH模型可用于预测不同气候带公共卫生实践中的ILI趋势,从而有助于流感控制和预防工作。
UNASSIGNED: Influenza is an acute respiratory infectious disease with a significant global disease burden. Additionally, the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions (NPIs) have introduced uncertainty to the spread of influenza. However, comparative studies on the performance of innovative models and approaches used for influenza prediction are limited. Therefore, this study aimed to predict the trend of influenza-like illness (ILI) in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance.
UNASSIGNED: The generalized additive model (GAM), deep learning hybrid model based on Gate Recurrent Unit (GRU), and autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model were established to predict the trends of ILI 1-, 2-, 3-, and 4-week-ahead in Beijing, Tianjin, Shanxi, Hubei, Chongqing, Guangdong, Hainan, and the Hong Kong Special Administrative Region in China, based on sentinel surveillance data from 2011 to 2019. Three relevant metrics, namely, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R squared, were calculated to evaluate and compare the goodness of fit and robustness of the three models.
UNASSIGNED: Considering the MAPE, RMSE, and R squared values, the ARMA-GARCH model performed best, while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China. Additionally, the models\' predictive performance declined as the weeks ahead increased. Furthermore, blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting.
UNASSIGNED: Our study suggested that the ARMA-GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model. Therefore, in the future, the ARMA-GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones, thereby contributing to influenza control and prevention efforts.