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Prediction and analysis of Corona Virus Disease 2019

Author

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  • Yan Hao
  • Ting Xu
  • Hongping Hu
  • Peng Wang
  • Yanping Bai

Abstract

The outbreak of Corona Virus Disease 2019 (COVID-19) in Wuhan has significantly impacted the economy and society globally. Countries are in a strict state of prevention and control of this pandemic. In this study, the development trend analysis of the cumulative confirmed cases, cumulative deaths, and cumulative cured cases was conducted based on data from Wuhan, Hubei Province, China from January 23, 2020 to April 6, 2020 using an Elman neural network, long short-term memory (LSTM), and support vector machine (SVM). A SVM with fuzzy granulation was used to predict the growth range of confirmed new cases, new deaths, and new cured cases. The experimental results showed that the Elman neural network and SVM used in this study can predict the development trend of cumulative confirmed cases, deaths, and cured cases, whereas LSTM is more suitable for the prediction of the cumulative confirmed cases. The SVM with fuzzy granulation can successfully predict the growth range of confirmed new cases and new cured cases, although the average predicted values are slightly large. Currently, the United States is the epicenter of the COVID-19 pandemic. We also used data modeling from the United States to further verify the validity of the proposed models.

Suggested Citation

  • Yan Hao & Ting Xu & Hongping Hu & Peng Wang & Yanping Bai, 2020. "Prediction and analysis of Corona Virus Disease 2019," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-15, October.
    • Handle: RePEc:plo:pone00:0239960
    DOI: 10.1371/journal.pone.0239960
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    References listed on IDEAS

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    1. Evan L Ray & Nicholas G Reich, 2018. "Prediction of infectious disease epidemics via weighted density ensembles," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-23, February.
    2. Bai, Yanping & Jin, Zhen, 2005. "Prediction of SARS epidemic by BP neural networks with online prediction strategy," Chaos, Solitons & Fractals, Elsevier, vol. 26(2), pages 559-569.
    3. Cleo Anastassopoulou & Lucia Russo & Athanasios Tsakris & Constantinos Siettos, 2020. "Data-based analysis, modelling and forecasting of the COVID-19 outbreak," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.
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    2. Khezar Hayat & Meagen Rosenthal & Sen Xu & Muhammad Arshed & Pengchao Li & Panpan Zhai & Gebrehaweria Kassa Desalegn & Yu Fang, 2020. "View of Pakistani Residents toward Coronavirus Disease (COVID-19) during a Rapid Outbreak: A Rapid Online Survey," IJERPH, MDPI, vol. 17(10), pages 1-10, May.
    3. Muqrin A. Almuqrin & Mukhtar M. Salah & Essam A. Ahmed, 2022. "Statistical Inference for Competing Risks Model with Adaptive Progressively Type-II Censored Gompertz Life Data Using Industrial and Medical Applications," Mathematics, MDPI, vol. 10(22), pages 1-38, November.
    4. Rujeerapaiboon, Napat & Zhong, Yuanguang & Zhu, Dan, 2023. "Resilience of long chain under disruption," European Journal of Operational Research, Elsevier, vol. 309(2), pages 597-615.
    5. Adam Goliński & Peter Spencer, 2021. "Modeling the Covid‐19 epidemic using time series econometrics," Health Economics, John Wiley & Sons, Ltd., vol. 30(11), pages 2808-2828, November.

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