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Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China

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  • Wei Wu
  • Junqiao Guo
  • Shuyi An
  • Peng Guan
  • Yangwu Ren
  • Linzi Xia
  • Baosen Zhou

Abstract

Background: Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS. Methods: Two hybrid models, one composed of nonlinear autoregressive neural network (NARNN) and autoregressive integrated moving average (ARIMA) the other composed of generalized regression neural network (GRNN) and ARIMA were constructed to predict the incidence of HFRS in the future one year. Performances of the two hybrid models were compared with ARIMA model. Results: The ARIMA, ARIMA-NARNN ARIMA-GRNN model fitted and predicted the seasonal fluctuation well. Among the three models, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA-NARNN hybrid model was the lowest both in modeling stage and forecasting stage. As for the ARIMA-GRNN hybrid model, the MSE, MAE and MAPE of modeling performance and the MSE and MAE of forecasting performance were less than the ARIMA model, but the MAPE of forecasting performance did not improve. Conclusion: Developing and applying the ARIMA-NARNN hybrid model is an effective method to make us better understand the epidemic characteristics of HFRS and could be helpful to the prevention and control of HFRS.

Suggested Citation

  • Wei Wu & Junqiao Guo & Shuyi An & Peng Guan & Yangwu Ren & Linzi Xia & Baosen Zhou, 2015. "Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-13, August.
    • Handle: RePEc:plo:pone00:0135492
    DOI: 10.1371/journal.pone.0135492
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    References listed on IDEAS

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    1. Xingyu Zhang & Yuanyuan Liu & Min Yang & Tao Zhang & Alistair A Young & Xiaosong Li, 2013. "Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-11, May.
    2. Lijing Yu & Lingling Zhou & Li Tan & Hongbo Jiang & Ying Wang & Sheng Wei & Shaofa Nie, 2014. "Application of a New Hybrid Model with Seasonal Auto-Regressive Integrated Moving Average (ARIMA) and Nonlinear Auto-Regressive Neural Network (NARNN) in Forecasting Incidence Cases of HFMD in Shenzhe," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-9, June.
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    1. Yongbin Wang & Chunjie Xu & Zhende Wang & Shengkui Zhang & Ying Zhu & Juxiang Yuan, 2018. "Time series modeling of pertussis incidence in China from 2004 to 2018 with a novel wavelet based SARIMA-NAR hybrid model," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-23, December.
    2. Kırbaş, İsmail & Sözen, Adnan & Tuncer, Azim Doğuş & Kazancıoğlu, Fikret Şinasi, 2020. "Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).

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