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

Askar Akaev, Alexander I. Zvyagintsev, Askar Sarygulov, Tessaleno Devezas, Andrea Tick and Yuri Ichkitidze
Additional contact information
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
Yuri Ichkitidze: Department of Finance, HSE University, 101000 St. Petersburg, Russia

Mathematics, 2022, vol. 10, issue 19, 1-18

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.

Keywords: COVID-19; economic systems; governance models; economic recovery; SIR model; Sanderson model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/19/3654/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/19/3654/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:19:p:3654-:d:934332

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().