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. 2016 Mar 23;13(4):355.
doi: 10.3390/ijerph13040355.

Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans

Lingling Zhou  1   2 , Jing Xia  3 , Lijing Yu  4 , Ying Wang  5 , Yun Shi  6 , Shunxiang Cai  7 , Shaofa Nie  8
Affiliations

Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans

Lingling Zhou et al. Int J Environ Res Public Health. .

Abstract

Background: 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.

Keywords: ARIMA model; NARNN model; forecasting; hybrid model; schistosomiasis.

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Figures

Figure 1
Figure 1
Autocorrelation function (ACF) and partial autocorrelation function (PACF) plots of original prevalence series (OS). (A) and (B) show ACF and PACF plots of OS (1956–2008). (C) and (D) show ACF and PACF plots after one order of differencing (1956–2008). (E) and (F) show ACF and PACF plots of OS (1956–2012). (G) and (H) show ACF and PACF plots after one order of differencing (1956–2012). Dotted lines indicate 95% confidence intervals.
Figure 2
Figure 2
Error autocorrelation plots of different target series from appropriate NARNN model. The red dotted line indicate 95% confidence intervals. All the coefficients fell within the 95% confidence limits with the exception of the autocorrelation coefficient at zero lag, indicating that the model reliably corresponds to the data. OS = original prevalence series, RS = residual series, NRS = new residual series.
Figure 3
Figure 3
Time series response plots of different target series from the appropriate NARNN model. (A–C) display the inputs, targets, and errors versus time and also give which time points were selected for training, testing, and validation.
Figure 4
Figure 4
The change trend plot of the prevalence of schistosomiasis in humans of Yangxin County. The black line represents the original prevalence series (1956–2012) and the red line represents the predicted prevalence series (1961–2016) from the ARIMA-NARNN model. The black dotted line gives the criteria of schistosomiasis transmission control in humans.
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