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Growth Recovery and COVID-19 Pandemic Model: Comparative Analysis for Selected Emerging Economies

Author

Listed:
  • Askar Akaev

    (Institute of Complex Systems Mathematical Research, Moscow State University, 119991 Moscow, Russia)

  • Alexander I. Zvyagintsev

    (Mikhailovskaya Military Artillery Academy, 195009 St. Petersburg, Russia)

  • Askar Sarygulov

    (Center for Fundamental Research, St. Petersburg State University of Economics, 191023 St. Petersburg, Russia)

  • Tessaleno Devezas

    (Engineering Faculty, Atlântica Instituto Universitário, 2730-036 Barcarena, Portugal)

  • Andrea Tick

    (Keleti Károly Faculty of Business and Management, Óbuda University, 1084 Budapest, Hungary)

  • Yuri Ichkitidze

    (Department of Finance, HSE University, 101000 St. Petersburg, Russia)

Abstract

The outburst of the COVID-19 pandemic and its rapid spread throughout the world in 2020 shed a new light on mathematic models describing the nature of epidemics. However, as the pandemic shocked economies to a much greater extent than earlier epidemics, the recovery potential of economies was emphasized and its inclusion in epidemic models is becoming more important. The present paper deals with the issues of modeling the recovery of economic systems that have undergone severe medical shocks, such as COVID-19. The proposed mathematical model considers the close relationship between the dynamics of pandemics and economic development. This distinguishes it from purely “medical” models, which are used exclusively to study the dynamics of the spread of the COVID-19 pandemic. Unlike standard SIR models, the present approach involves the introduction of the “vaccine” equation to the SIR model and introduces correction components that include the possibility of re-infection and other nuances such as the number of people at risk of infection (not sick with COVID but not vaccinated); sick with COVID; recovered; fully vaccinated (two doses) citizens; the rate of COVID infection; the rate of recovery of infected individuals; the vaccination coefficients, respectively, for those who have not been ill and recovered from COVID; the coefficient of revaccination; the COVID re-infection rate; and the population fluctuation coefficient, which takes into account the effect of population change as a result of births and deaths and due to the departure and return of citizens. The present model contains governance so that it not only generates scenario projections but also models specific governance measures as well to include the pandemic and restore economic growth. The model also adds management issues, so that it not only generates scenario forecasts but simultaneously models specific management measures as well, aiming to suppress the pandemic and restoring economic growth. The model was implemented on specific data on the dynamics of the spread of the COVID-19 pandemic in selected developing economies.

Suggested Citation

  • Askar Akaev & Alexander I. Zvyagintsev & Askar Sarygulov & Tessaleno Devezas & Andrea Tick & Yuri Ichkitidze, 2022. "Growth Recovery and COVID-19 Pandemic Model: Comparative Analysis for Selected Emerging Economies," Mathematics, MDPI, vol. 10(19), pages 1-18, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3654-:d:934332
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    References listed on IDEAS

    as
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