[Prediction of schistosomiasis infection rates of population based on ARIMA-NARNN model].

OBJECTIVE

To explore the effect of the autoregressive integrated moving average model-nonlinear auto-regressive neural network (ARIMA-NARNN) model on predicting schistosomiasis infection rates of population.

METHODS

The ARIMA model, NARNN model and ARIMA-NARNN model were established based on monthly schistosomiasis infection rates from January 2005 to February 2015 in Jiangsu Province, China. The fitting and prediction performances of the three models were compared.

RESULTS

Compared to the ARIMA model and NARNN model, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model were the least with the values of 0.011 1, 0.090 0 and 0.282 4, respectively.

CONCLUSIONS

The ARIMA-NARNN model could effectively fit and predict schistosomiasis infection rates of population, which might have a great application value for the prevention and control of schistosomiasis.