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Open AccessArticle

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

by Lingling Zhou 1,2,†, Jing Xia 3,†, Lijing Yu 1, Ying Wang 1, Yun Shi 1, Shunxiang Cai 3,* and Shaofa Nie 1,*
1
Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China
2
Department of Information, Research Institute of Field Surgery, Daping Hospital of Third Military Medical University, Chongqing 400042, China
3
Institute of Parasitic Disease Control, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Peter Congdon
Int. J. Environ. Res. Public Health 2016, 13(4), 355; https://doi.org/10.3390/ijerph13040355
Received: 2 December 2015 / Revised: 25 January 2016 / Accepted: 29 February 2016 / Published: 23 March 2016
(This article belongs to the Special Issue Spatio-temporal Frameworks for Infectious Disease Epidemiology)
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. View Full-Text
Keywords: schistosomiasis; forecasting; ARIMA model; NARNN model; hybrid model schistosomiasis; forecasting; ARIMA model; NARNN model; hybrid model
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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.

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