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Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China

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  • Xingyu Zhang
  • Yuanyuan Liu
  • Min Yang
  • Tao Zhang
  • Alistair A Young
  • Xiaosong Li

Abstract

Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0063116
    DOI: 10.1371/journal.pone.0063116
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    1. Qinqin Xu & Runzi Li & Yafei Liu & Cheng Luo & Aiqiang Xu & Fuzhong Xue & Qing Xu & Xiujun Li, 2017. "Forecasting the Incidence of Mumps in Zibo City Based on a SARIMA Model," IJERPH, MDPI, vol. 14(8), pages 1-11, August.
    2. Khan, Firdos & Saeed, Alia & Ali, Shaukat, 2020. "Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Xiaoxin Zhu & Yanyan Wang & David Regan & Baiqing Sun, 2020. "A Quantitative Study on Crucial Food Supplies after the 2011 Tohoku Earthquake Based on Time Series Analysis," IJERPH, MDPI, vol. 17(19), pages 1-13, September.
    4. 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.
    5. Sharadga, Hussein & Hajimirza, Shima & Balog, Robert S., 2020. "Time series forecasting of solar power generation for large-scale photovoltaic plants," Renewable Energy, Elsevier, vol. 150(C), pages 797-807.
    6. Md. Abul Kalam Azad & Abu Reza Md. Towfiqul Islam & Md. Siddiqur Rahman & Kurratul Ayen, 2021. "Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(1), pages 1109-1135, August.
    7. Yongqing Zhao & Rendong Li & Juan Qiu & Xiangdong Sun & Lu Gao & Mingquan Wu, 2019. "Prediction of Human Brucellosis in China Based on Temperature and NDVI," IJERPH, MDPI, vol. 16(21), pages 1-15, November.
    8. Vaishnav, Vaibhav & Vajpai, Jayashri, 2020. "Assessment of impact of relaxation in lockdown and forecast of preparation for combating COVID-19 pandemic in India using Group Method of Data Handling," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    9. Ahmad M Awajan & Mohd Tahir Ismail & S AL Wadi, 2018. "Improving forecasting accuracy for stock market data using EMD-HW bagging," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-20, July.
    10. Xingyu Zhang & Tao Zhang & Jiao Pei & Yuanyuan Liu & Xiaosong Li & Pau Medrano-Gracia, 2016. "Time Series Modelling of Syphilis Incidence in China from 2005 to 2012," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-18, February.

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