Mathematical Modeling of Overcoming the COVID-19 Pandemic and Restoring Economic Growth

Sadovnichiy, V. A.; Akaev, Askar; Zvyagintsev, A. I.; Sarygulov, Askar

doi:10.1134/s1064562422040160

Cited by 1 publication

(3 citation statements)

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“…The periodic orbit modeled by system (4) with the initial conditions given in ( 5) based on the statistical data on COVID-19 for Russia was verified in Sadovnichiy, Akaev, Zvyagintsev, and Sarygulov [30]. In this case, the normalization coefficients B 1 , C 1 , B 2 , C 2 were chosen in such a way that the trajectories B 1 I j + C 1 and B 2 R j + C 2 fluctuated, respectively, from minI j to maxI j and from minR j to maxR j .…”

Section: Extension Of the Sir Modelmentioning

confidence: 78%

“…The possibility of such a modification was proved in [29]. Thus, our work, based on statistical data on the coronavirus pandemic in Russia [30], contains monthly indicators of the incidence and recovery of Russian citizens during the coronavirus pandemic and shows that the dynamics of incidence and recovery has waves with peaks repeated with a frequency of 7 months. The proposed model adds a "vaccine" equation and transforms the differential equations to difference equations, thus creating an extended model.…”

Section: The Kermack-mckendrick Modelmentioning

confidence: 87%

“…Zvyagintsev [29] has shown that such a model allows the generation of cyclic trajectories with the required periodicity, which is a period of 7 months in the present case. Verification of the periodic orbit with specific initial conditions based on the statistical data on COVID-19 for Russia was conducted in Sadovnichiy et al [30]. The proposed model is then combined with the "Wonderland model" [22,23] and then, to overcome the chaotic dynamics and the random characteristics of morbidity and recovery, the modern theory of chaos control is used [31,32] and a controlling function is determined.…”

Section: The Kermack-mckendrick Modelmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Extension Of the Sir Modelmentioning

confidence: 78%

Section: The Kermack-mckendrick Modelmentioning

confidence: 87%