Abstract：Objective To compare the efficiency of autoregressive integrated moving average (ARIMA) model and exponential smoothing method to predict cases of influenza-like illness (ILI%) in northern China, and to provide a scientific basis for preventing and controlling influenza. Methods Data concerning the weeklypercentage of outpatient visits forinfluenza-like illness (ILI) in northern China between the first week of 2012 and the seventeenth week of 2018 were collected to fit and establish ARIMA model and exponential smoothing model. The weekly ILI% from the eighteenth week of 2018 to the seventeenth week of 2019 was predicted, and then the predictive value was compared with the actual value. Results The optimal ARIMA model was ARIMA(0,1,1)(2,1,0)52, and root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) of the predictive value were 0.57%, 8.98% and 0.34%, respectively. Simple seasonal model was the optimal model based on exponential smoothing method, and RMSE, MAPE and MAE of the predictive value were 0.83%, 15.24% and 0.55%, respectively. Conclusions ARIMA (0,1,1)(2,1,0)52 has a higher forecast precision, and it can be used for short-term prediction of ILI cases in northern China.
许俐颖, 秦国友. ARIMA模型和指数平滑法在我国北方流感样病例预测中的应用和比较[J]. 实用预防医学, 2020, 27(7): 889-892.
XU Li-ying, QIN Guo-you. Application and comparison of ARIMA model and exponential smoothing methodto the prediction of influenza-like illness cases in northern China. , 2020, 27(7): 889-892.
2020, Vol. 27 
