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The Application of the Grey Disaster Model to Forecast Epidemic Peaks of Typhoid and Paratyphoid Fever in China

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  • Xuejun Shen
  • Limin Ou
  • Xiaojun Chen
  • Xin Zhang
  • Xuerui Tan

Abstract

Objective: The objectives of this study were to forecast epidemic peaks of typhoid and paratyphoid fever in China using the grey disaster model, to evaluate its feasibility of predicting the epidemic tendency of notifiable diseases. Methods: According to epidemiological features, the GM(1,1) model and DGM model were used to build the grey disaster model based on the incidence data of typhoid and paratyphoid fever collected from the China Health Statistical Yearbook. Model fitting accuracy test was used to evaluate the performance of these two models. Then, the next catastrophe date was predicted by the better model. Results: The simulation results showed that DGM model was better than GM(1,1) model in our data set. Using the DGM model, we predicted the next epidemic peak time will occur between 2023 to 2025. Conclusion: The grey disaster model can predict the typhoid and paratyphoid fever epidemic time precisely, which may provide valuable information for disease prevention and control.

Suggested Citation

  • Xuejun Shen & Limin Ou & Xiaojun Chen & Xin Zhang & Xuerui Tan, 2013. "The Application of the Grey Disaster Model to Forecast Epidemic Peaks of Typhoid and Paratyphoid Fever in China," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-6, April.
    • Handle: RePEc:plo:pone00:0060601
    DOI: 10.1371/journal.pone.0060601
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    Cited by:

    1. Atif Maqbool Khan & Magdalena Osińska, 2021. "How to Predict Energy Consumption in BRICS Countries?," Energies, MDPI, vol. 14(10), pages 1-21, May.
    2. Adedayo Ajayi & Patrick Chi-Kwong Luk & Liyun Lao & Mohammad Farhan Khan, 2023. "Energy Forecasting Model for Ground Movement Operation in Green Airport," Energies, MDPI, vol. 16(13), pages 1-19, June.
    3. R. Rajesh & Chandrasekharan Rajendran, 2019. "Grey- and rough-set-based seasonal disaster predictions: an analysis of flood data in India," 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. 97(1), pages 395-435, May.
    4. Qingwei Xu & Kaili Xu, 2020. "Statistical Analysis and Prediction of Fatal Accidents in the Metallurgical Industry in China," IJERPH, MDPI, vol. 17(11), pages 1-20, May.
    5. Altay, Nezih & Narayanan, Arunachalam, 2022. "Forecasting in humanitarian operations: Literature review and research needs," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1234-1244.
    6. Sanubari Tansah Tresna & Subiyanto & Sudradjat Supian, 2022. "Mathematical Models for Typhoid Disease Transmission: A Systematic Literature Review," Mathematics, MDPI, vol. 10(14), pages 1-12, July.

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