Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans
AbstractBackground: We previously proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in forecasting schistosomiasis. Our purpose in the current study was to forecast the annual prevalence of human schistosomiasis in Yangxin County, using our ARIMA-NARNN model, thereby further certifying the reliability of our hybrid model. Methods: We used the ARIMA, NARNN and ARIMA-NARNN models to fit and forecast the annual prevalence of schistosomiasis. The modeling time range included was the annual prevalence from 1956 to 2008 while the testing time range included was from 2009 to 2012. The mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to measure the model performance. We reconstructed the hybrid model to forecast the annual prevalence from 2013 to 2016. Results: The modeling and testing errors generated by the ARIMA-NARNN model were lower than those obtained from either the single ARIMA or NARNN models. The predicted annual prevalence from 2013 to 2016 demonstrated an initial decreasing trend, followed by an increase. Conclusions: The ARIMA-NARNN model can be well applied to analyze surveillance data for early warning systems for the control and elimination of schistosomiasis. View Full-Text
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Zhou, L.; Xia, J.; Yu, L.; Wang, Y.; Shi, Y.; Cai, S.; Nie, S. Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans. Int. J. Environ. Res. Public Health 2016, 13, 355.
Zhou L, Xia J, Yu L, Wang Y, Shi Y, Cai S, Nie S. Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans. International Journal of Environmental Research and Public Health. 2016; 13(4):355.Chicago/Turabian Style
Zhou, Lingling; Xia, Jing; Yu, Lijing; Wang, Ying; Shi, Yun; Cai, Shunxiang; Nie, Shaofa. 2016. "Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans." Int. J. Environ. Res. Public Health 13, no. 4: 355.
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