Prediction of rabies cases in China by using autoregressive moving average model

ObjectiveTo predict the monthly incidence of rabies in the mainland of China by using autoregressive moving average model (ARIMA), and provide reference for the prevention and control of rabies in China.

MethodsUsing SPSS 19.0 software, a time series model was established by using the monthly incidence data of rabies in China from January 2007 to December 2016, and the optimal model was validated by the monthly incidence data of rabies from January to December 2017. The optimal model was used to predict the incidence trend and case number of rabies in 2018.

ResultsThe optimal model was ARIMA(0,1,1)(2,1,0)₁₂, with a stationary R²＝0.539, RMSE＝17.653, Ljung-Box Q＝8.932, P＝0.881. In predicting the data for January-December 2017, the relative error of prediction was 1.55%. A total of 516 rabies cases occurred actually in 2017. It was predicted that the case number of rabies in China would drop to 398 in 2018.

ConclusionThe ARIMA(0,1,1)(2,1,0)₁₂ model can well fit the long-term trend and seasonal trend of rabies incidence, and the results of retrograde fitting and short-term prediction are ideal.