ABSTRACT
Objective To predict the monthly reported echinococcosis cases in China with the autoregressive integrated mov-ing average(ARIMA)model,so as to provide a reference for prevention and control of echinococcosis. Methods SPSS 24.0 software was used to construct the ARIMA models based on the monthly reported echinococcosis cases of time series from 2007 to 2015 and 2007 to 2014,respectively,and the accuracies of the two ARIMA models were compared. Results The model based on the data of the monthly reported cases of echinococcosis in China from 2007 to 2015 was ARIMA(1,0,0)(1,1, 0)12,the relative error among reported cases and predicted cases was-13.97%,AR(1)=0.367(t=3.816,P<0.001),SAR (1)=-0.328(t=-3.361,P=0.001),and Ljung-Box Q=14.119(df=16,P=0.590).The model based on the data of the monthly reported cases of echinococcosis in China from 2007 to 2014 was ARIMA(1,0,0)(1,0,1)12,the relative error among reported cases and predicted cases was 0.56%,AR(1)=0.413(t=4.244,P<0.001),SAR(1)=0.809(t=9.584, P<0.001),SMA(1)=0.356(t=2.278,P=0.025),and Ljung-Box Q=18.924(df=15,P=0.217).Conclusions The different time series may have different ARIMA models as for the same infectious diseases.It is needed to be further verified that the more data are accumulated,the shorter time of predication is,and the smaller the average of the relative error is.The estab-lishment and prediction of an ARIMA model is a dynamic process that needs to be adjusted and optimized continuously accord-ing to the accumulated data,meantime,we should give full consideration to the intensity of the work related to infectious diseas-es reported(such as disease census and special investigation).
ABSTRACT
Objective To study the application of autoregressive integrated moving average(ARIMA)model to predict the monthly reported malaria cases in China,so as to provide a reference for prevention and control of malaria. Methods SPSS 24.0 software was used to construct the ARIMA models based on the monthly reported malaria cases of the time series of 2006-2015 and 2011-2015,respectively. The data of malaria cases from January to December,2016 were used as validation data to compare the accuracy of the two ARIMA models. Results The models of the monthly reported cases of malaria in China were ARIMA(2,1,1)(1,1,0)12 and ARIMA(1,0,0)(1,1,0)12 respectively. The comparison between the predictions of the two models and actual situation of malaria cases showed that the ARIMA model based on the data of 2011-2015 had a higher ac-curacy of forecasting than the model based on the data of 2006-2015 had. Conclusion The establishment and prediction of ARIMA model is a dynamic process,which needs to be adjusted unceasingly according to the accumulated data,and in addi-tion,the major changes of epidemic characteristics of infectious diseases must be considered.