Application of autoregressive integrated moving average model in predicting incidence of bacillary dysentery in Shaanxi

Objective To evaluate the feasibility of time series model to predict the incidence of infectious diseases. Methods According to the time series of reported monthly incidence of bacillary dysentery in Shaanxi province from 2004 to 2012, the autoregressive integrated moving average (ARIMA) model was established by using the incidence data of bacillary dysentery from January to December 2013 as demonstration data. The predictive power of ARIMA model was evaluated. Results The case curve is not only with a long-term descending trend but also with annual seasonality. The relative optimum fitting model was ARIMA(0,1,1)(1,1,0)12. Ljung-Box Q had no statistical significance (Ljung-Box Q=21.994,P=0.143) and residuals was the white noise. The average of the relative error between actual value and predicted value from January to December in 2013 was 20.75% (maximum 40.37%, minimum 4.94%). Conclusion The ARIMA model can be used to effectively predict the incidence of bacillary dysentery in Shaanxi. More original data are needed in order to optimize the model.