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Prediction of the Number of Patients Infected with COVID-19 Based on Rolling Grey Verhulst Models

Author

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  • Yu-Feng Zhao

    (School of Economics, Zhejiang University of Finance & Economics, Hangzhou 310018, China)

  • Ming-Huan Shou

    (School of Economics, Zhejiang University of Finance & Economics, Hangzhou 310018, China)

  • Zheng-Xin Wang

    (School of Economics, Zhejiang University of Finance & Economics, Hangzhou 310018, China)

Abstract

The outbreak of a novel coronavirus (SARS-CoV-2) has caused a large number of residents in China to be infected with a highly contagious pneumonia recently. Despite active control measures taken by the Chinese government, the number of infected patients is still increasing day by day. At present, the changing trend of the epidemic is attracting the attention of everyone. Based on data from 21 January to 20 February 2020, six rolling grey Verhulst models were built using 7-, 8- and 9-day data sequences to predict the daily growth trend of the number of patients confirmed with COVID-19 infection in China. The results show that these six models consistently predict the S-shaped change characteristics of the cumulative number of confirmed patients, and the daily growth decreased day by day after 4 February. The predicted results obtained by different models are very approximate, with very high prediction accuracy. In the training stage, the maximum and minimum mean absolute percentage errors (MAPEs) are 4.74% and 1.80%, respectively; in the testing stage, the maximum and minimum MAPEs are 4.72% and 1.65%, respectively. This indicates that the predicted results show high robustness. If the number of clinically diagnosed cases in Wuhan City, Hubei Province, China, where COVID-19 was first detected, is not counted from 12 February, the cumulative number of confirmed COVID-19 cases in China will reach a maximum of 60,364–61,327 during 17–22 March; otherwise, the cumulative number of confirmed cases in China will be 78,817–79,780.

Suggested Citation

  • Yu-Feng Zhao & Ming-Huan Shou & Zheng-Xin Wang, 2020. "Prediction of the Number of Patients Infected with COVID-19 Based on Rolling Grey Verhulst Models," IJERPH, MDPI, vol. 17(12), pages 1-20, June.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:12:p:4582-:d:376409
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    References listed on IDEAS

    as
    1. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    2. Mohammed A. A. Al-qaness & Ahmed A. Ewees & Hong Fan & Mohamed Abd Elaziz, 2020. "Optimized Forecasting Method for Weekly Influenza Confirmed Cases," IJERPH, MDPI, vol. 17(10), pages 1-12, May.
    3. Qian Liu & Bo Li & Muhammad Mohiuddin, 2018. "Prediction and Decomposition of Efficiency Differences in Chinese Provincial Community Health Services," IJERPH, MDPI, vol. 15(10), pages 1-14, October.
    4. Liping Zhang & Li Wang & Yanling Zheng & Kai Wang & Xueliang Zhang & Yujian Zheng, 2017. "Time Prediction Models for Echinococcosis Based on Gray System Theory and Epidemic Dynamics," IJERPH, MDPI, vol. 14(3), pages 1-14, March.
    5. Xu, Ning & Ding, Song & Gong, Yande & Bai, Ju, 2019. "Forecasting Chinese greenhouse gas emissions from energy consumption using a novel grey rolling model," Energy, Elsevier, vol. 175(C), pages 218-227.
    6. Ling-Ling Pei & Qin Li & Zheng-Xin Wang, 2018. "The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China," IJERPH, MDPI, vol. 15(3), pages 1-17, March.
    7. Hua’an Wu & Bo Zeng & Meng Zhou, 2017. "Forecasting the Water Demand in Chongqing, China Using a Grey Prediction Model and Recommendations for the Sustainable Development of Urban Water Consumption," IJERPH, MDPI, vol. 14(11), pages 1-12, November.
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