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

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  • Lingling Zhou

    (Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China
    Department of Information, Research Institute of Field Surgery, Daping Hospital of Third Military Medical University, Chongqing 400042, China
    These authors contributed equally to this work.)

  • Jing Xia

    (Institute of Parasitic Disease Control, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China
    These authors contributed equally to this work.)

  • Lijing Yu

    (Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China)

  • Ying Wang

    (Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China)

  • Yun Shi

    (Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China)

  • Shunxiang Cai

    (Institute of Parasitic Disease Control, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China)

  • Shaofa Nie

    (Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China)

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.

Suggested Citation

  • Lingling Zhou & Jing Xia & Lijing Yu & Ying Wang & Yun Shi & Shunxiang Cai & Shaofa Nie, 2016. "Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans," IJERPH, MDPI, vol. 13(4), pages 1-13, March.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:4:p:355-:d:66323
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    References listed on IDEAS

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    2. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
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