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CBRR Model for Predicting the Dynamics of the COVID-19 Epidemic in Real Time

Author

Listed:
  • Victor Zakharov

    (Faculty of Applied Mathematics and Control Processes, Saint Petersburg State University, Universitetskaya Naberezhnaya 7–9, 199034 Saint Petersburg, Russia)

  • Yulia Balykina

    (Faculty of Applied Mathematics and Control Processes, Saint Petersburg State University, Universitetskaya Naberezhnaya 7–9, 199034 Saint Petersburg, Russia)

  • Ovanes Petrosian

    (Faculty of Applied Mathematics and Control Processes, Saint Petersburg State University, Universitetskaya Naberezhnaya 7–9, 199034 Saint Petersburg, Russia
    School of Automation, Qingdao University, 308 Ningxia Road, Qingdao 266071, China)

  • Hongwei Gao

    (School of Mathematics and Statistics, Qingdao University, 308 Ningxia Road, Qingdao 266071, China)

Abstract

Because of the lack of reliable information on the spread parameters of COVID-19, there is an increasing demand for new approaches to efficiently predict the dynamics of new virus spread under uncertainty. The study presented in this paper is based on the Case-Based Reasoning method used in statistical analysis, forecasting and decision making in the field of public health and epidemiology. A new mathematical Case-Based Rate Reasoning model (CBRR) has been built for the short-term forecasting of coronavirus spread dynamics under uncertainty. The model allows for predicting future values of the increase in the percentage of new cases for a period of 2–3 weeks. Information on the dynamics of the total number of infected people in previous periods in Italy, Spain, France, and the United Kingdom was used. Simulation results confirmed the possibility of using the proposed approach for constructing short-term forecasts of coronavirus spread dynamics. The main finding of this study is that using the proposed approach for Russia showed that the deviation of the predicted total number of confirmed cases from the actual one was within 0.3%. For the USA, the deviation was 0.23%.

Suggested Citation

  • Victor Zakharov & Yulia Balykina & Ovanes Petrosian & Hongwei Gao, 2020. "CBRR Model for Predicting the Dynamics of the COVID-19 Epidemic in Real Time," Mathematics, MDPI, vol. 8(10), pages 1-10, October.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:10:p:1727-:d:424959
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    References listed on IDEAS

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    Cited by:

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    2. Ramani, Vinay & Ghosh, Debabrata & Sodhi, ManMohan S., 2022. "Understanding systemic disruption from the Covid-19-induced semiconductor shortage for the auto industry," Omega, Elsevier, vol. 113(C).
    3. Rozhkov, Maxim & Ivanov, Dmitry & Blackhurst, Jennifer & Nair, Anand, 2022. "Adapting supply chain operations in anticipation of and during the COVID-19 pandemic," Omega, Elsevier, vol. 110(C).

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