背景:季节性流感在中国是一个严重的公共卫生问题。本研究旨在建立一种新的季节性流感发病预测混合模型,为疫情爆发前的预警管理提供参考信息。
方法:2004-2018年季节性流感月发病率数据来源于中国公共卫生科学数据中心网站。建立了单季节自回归综合移动平均(SARIMA)模型和单误差趋势和季节性(ETS)模型。在此基础上,我们建造了SARIMA,ETS,和支持向量回归(SARIMA-ETS-SVR)混合模型。通过比较平均绝对误差(MAE)来确定预测性能,均方误差(MSE),平均绝对百分比误差(MAPE),和均方根误差(RMSE)指数。
结果:最佳SARIMA模型为SARIMA(0,1,0)(0,0,1)12。误差趋势和季节性(ETS)(M,A,M)是SARIMA最优模型。对于配件性能,SARIMA-ETS-SVR混合模型实现了MAE的最低值,MSE,和RMSE,除了地图。在预测性能方面,SARIMA-ETS-SVR混合模型的MAE最低,MSE,地图,和RMSE值在三个模型中。
结论:研究表明,SARIMA-ETS-SVR混合模型比单个SARIMA模型和单个ETS模型具有更好的泛化能力,预测将为预防这种传染病提供有用的工具。
Seasonal influenza is a serious public health issue in China. This study aimed to develop a new hybrid model for seasonal influenza incidence prediction and provide reference information for early warning management before outbreaks.
Data on the monthly incidence of seasonal influenza between 2004 and 2018 were obtained from the China Public Health Science Data Center website. A single seasonal autoregressive integrated moving average (SARIMA) model and a single error trend and seasonality (ETS) model were built. On this basis, we constructed SARIMA,
ETS, and support vector regression (SARIMA-
ETS-SVR) hybrid model. The prediction performance was determined by comparing mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE) indices.
The optimum SARIMA model was SARIMA (0,1,0) (0,0,1)12. Error trend and seasonality (
ETS) (M,A,M) was the SARIMA optimal model. For the fitting performance, the SARIMA-
ETS-SVR hybrid model achieved the lowest values of MAE, MSE, and RMSE, in addition to the MAPE. In terms of predictive performance, the SARIMA-
ETS-SVR hybrid model had the lowest MAE, MSE, MAPE, and RMSE values among the three models.
The study demonstrated that the SARIMA-
ETS-SVR hybrid model provides better generalization ability than a single SARIMA model and a single ETS model, and the predictions will provide a useful tool for preventing this infectious disease.