[Study on the ARIMA model application to predict echinococcosis cases in China].

OBJECTIVE

To predict the monthly reported echinococcosis cases in China with the autoregressive integrated moving average (ARIMA) model, so as to provide a reference for prevention and control of echinococcosis.

METHODS

SPSS 24.0 software was used to construct the ARIMA models based on the monthly reported echinococcosis cases of time series from 2007 to 2015 and 2007 to 2014, respectively, and the accuracies of the two ARIMA models were compared.

RESULTS

The model based on the data of the monthly reported cases of echinococcosis in China from 2007 to 2015 was ARIMA (1, 0, 0) (1, 1, 0)12, the relative error among reported cases and predicted cases was -13.97%, AR (1) = 0.367 (t = 3.816, P < 0.001), SAR (1) = -0.328 (t = -3.361, P = 0.001), and Ljung-Box Q = 14.119 (df = 16, P = 0.590) . The model based on the data of the monthly reported cases of echinococcosis in China from 2007 to 2014 was ARIMA (1, 0, 0) (1, 0, 1)12, the relative error among reported cases and predicted cases was 0.56%, AR (1) = 0.413 (t = 4.244, P < 0.001), SAR (1) = 0.809 (t = 9.584, P < 0.001), SMA (1) = 0.356 (t = 2.278, P = 0.025), and Ljung-Box Q = 18.924 (df = 15, P = 0.217).

CONCLUSIONS

The different time series may have different ARIMA models as for the same infectious diseases. It is needed to be further verified that the more data are accumulated, the shorter time of predication is, and the smaller the average of the relative error is. The establishment and prediction of an ARIMA model is a dynamic process that needs to be adjusted and optimized continuously according to the accumulated data, meantime, we should give full consideration to the intensity of the work related to infectious diseases reported (such as disease census and special investigation).