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

Objective To understand the feasibility of using multiple seasonal autoregressive integrated moving average (ARIMA) model to predict the monthly incidence of hand foot and mouth disease (HFMD). Methods ARIMA model was established by using surveillance data of HFMD in Nanchang from January 1 2009 to December 31 2012, and the prediction results were evaluated. Results Multiple seasonal ARIMA (0,1,1) (1,1,0)12 model was established for the prediction of HFMD incidence, and the result of normal BIC was 12.31. Conclusion The multiple seasonal ARIMA model established had good fitting and prediction power fo the monthly incidence of HFMD in Nanchang, which can be used in the prevention and control of HFMD.