Application of multiple seasonal autoregressive integrated moving average model in prediction of incidence of hand foot and mouth disease in China

Objective To predict the incidence of hand foot and mouth disease(HFMD)in China by using multiple seasonal autoregressive integrated moving average(ARIMA)model, and provide scientific evidence for the improvement of HFMD prevention and control. Methods The ARIMA model was established based on the monthly case numbers of HFMD in China from January 2009 to June 2013,which was collected from national disease reporting information system, by using SPSS 19.0 software. The model was used to predict the incidence of HFMD during January-June 2013. Results The annual incidence peak of HFMD occurred during May-June. There were significant difference between the fitted multiple seasonal moving average coefficients and the non-seasonal moving average coefficients(0.532). Through the test of parameters and goodness of fit as well as white-noise residuals, we established the ARIMA(0,1,2)(0,1,0)12, of which Bayesian Information Criterion(BIC)=21.955 and the mean error of the model was 0.52. The model was not fitted well. After the provinces were categorized into two strata by the incidence pattern of HFMD, the ARIMA was employed to fit two models respectively, the prediction was improved and the mean error of the model was 0.12. Conclusion Time series analysis for historical reporting data is an important tool for communicable disease surveillance. ARIMA model is suitable to predict the incidence of HFMD in China, but due to the different incidence patterns of HFMD in different provinces the prediction can be improved by fitting different ARIMA model.