To compare the stochastic autoregressive integrated moving average (ARIMA) model and the grey system GM(1,1) model to predict the hepatitis B incidence in Qianan. Considering the Box-Jenkins modeling and GM(1,1) model approach, hepatitis B incidence was collected monthly from 2004 to 2011, a SARIMA model and a gray system GM(1,1) model were fit. Then, these models were used for calculating hepatitis B incidence for the last 6 observations compared with observed data. The constructed models were performed to predict the monthly incidence rate in 2013. The model SARIMA(0,1,1)(0,1,1)12 and was established finally and the residual sequence was a white noise sequence. Using Excel 2003 to establish the gray system GM(1,1) model of hepatitis B incidence and evaluating the accuracy of the mode as well as forecasting. By posterior-error-test (C=0.435, p=0.821) and residual test, the model accuracy was qualified. It was necessary and practical to apply the approach of ARIMA model in fitting time series to predict hepatitis within a short lead time. The prediction results showed that the hepatitis B incidence in 2013 had a slight upward trend.

The main objective of this study is to identify the stochastic autoregressive integrated moving average (ARIMA) model to predict the pulmonary tuberculosis incidence in Qianan. Considering the Box-Jenkins modeling approach, the incidence of pulmonary tuberculosis was collected monthly from 2004 to 2010. The model ARIMA(0,1,1)12 was established finally and the residual sequence was a white noise sequence. Then, this model was used for calculating dengue incidence for the last 6 observations compared with observed data, and performed to predict the monthly incidence in 2011. It is necessary and practical to apply the approach of ARIMA model in fitting time series to predict pulmonary tuberculosis within a short lead time.