关键词: 流行性腮腺炎, SARIMA, 指数平滑模型, GRNN, 组合模型, 预测

Abstract: Background　Mumps is a serious public health concern in China.It is necessary to fully understand the regular pattern of mumps，and then model and forecast the disease to provide the scientific theoretical evidences for its prevention and control.Objective　To evaluate the effect of seasonal autoregressive integrated moving average ( SARIMA ) model，exponential smoothing model，SARIMA- Generalized Regression Neural Network ( GRNN ) combined model and exponential smoothing-GRNN combined model in the fitting and prediction of mumps in China.Methods　The monthly incidence from January 2004 to June 2016 in China was used to fit and train the models，and SARIMA，exponential smoothing model，SARIMA-GRNN combined model and exponential smoothing-GRNN combined model were established respectively.The monthly incidence from July to December 2016 was predicted and compared with the actual value.Four evaluation indicators，such as mean absolute percentage error ( MAPE )，mean error rate ( MER )，mean square error ( MSE )，and mean absolute error ( MAE )，were used to evaluate the fitting and prediction effects of the models.Results　The SARIMA（0，0，2）（0，1，1）12 model was the most appropriate SARIMA model.The Holt-Winters multiplication model was the optimal exponential smoothing model.The SPREAD optimal parameters of the SARIMA-GRNN combined model and the exponential smoothing-GRNN combined model were 0.026 and 0.031，respectively.In the fitting phase，the MAPE，MER，MSE and MAE fitted by SARIMA，exponential smoothing model，SARIMA-GRNN combined model and exponential smoothing-GRNN combined model were 15.350%，14.976%，0.336，0.286；14.346%，14.249%，0.326，0.272；7.390%，6.320%，0.034，0.123；6.952%，5.776%，0.028，0.113，respectively.The MAPE，MER，MSE and MAE predicted by SARIMA，exponential smoothing model，SARIMA-GRNN combined model and exponential smoothing-GRNN combined model were 11.998%，12.260%，0.022，0.138；39.582%，38.462%，0.199，0.432；8.892%，9.677%，0.020，0.109；8.872%，9.672%，0.021，0.109，respectively.Conclusion　The exponential smoothing-GRNN combined model was the optimal model for predicting the incidence of mumps in China with higher accuracy，followed by SARIMA-GRNN combined model.The SARIMA model has poor fitting and prediction accuracy，and the exponential smoothing model has good fitting accuracy，but its prediction accuracy is poor.