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Forecasting a New Type of Virus Spread: A Case Study of COVID-19 with Stochastic Parameters

Author

Listed:
  • Victor Zakharov

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

  • Yulia Balykina

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

  • Igor Ilin

    (Graduate School of Business Engineering, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia)

  • Andrea Tick

    (Keleti Károly Faculty of Business and Management, Óbuda University, 1034 Budapest, Hungary)

Abstract

The consideration of infectious diseases from a mathematical point of view can reveal possible options for epidemic control and fighting the spread of infection. However, predicting and modeling the spread of a new, previously unexplored virus is still difficult. The present paper examines the possibility of using a new approach to predicting the statistical indicators of the epidemic of a new type of virus based on the example of COVID-19. The important result of the study is the description of the principle of dynamic balance of epidemiological processes, which has not been previously used by other researchers for epidemic modeling. The new approach is also based on solving the problem of predicting the future dynamics of precisely random values of model parameters, which is used for defining the future values of the total number of: cases (C); recovered and dead (R); and active cases (I). Intelligent heuristic algorithms are proposed for calculating the future trajectories of stochastic parameters, which are called the percentage increase in the total number of confirmed cases of the disease and the dynamic characteristics of epidemiological processes. Examples are given of the application of the proposed approach for making forecasts of the considered indicators of the COVID-19 epidemic, in Russia and European countries, during the first wave of the epidemic.

Suggested Citation

  • Victor Zakharov & Yulia Balykina & Igor Ilin & Andrea Tick, 2022. "Forecasting a New Type of Virus Spread: A Case Study of COVID-19 with Stochastic Parameters," Mathematics, MDPI, vol. 10(20), pages 1-18, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3725-:d:938847
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    References listed on IDEAS

    as
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    3. B. F. Finkenstädt & B. T. Grenfell, 2000. "Time series modelling of childhood diseases: a dynamical systems approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(2), pages 187-205.
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