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Simulation of coronavirus disease 2019 (COVID-19) scenarios with possibility of reinfection

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  • Malkov, Egor

Abstract

Epidemiological models of COVID-19 transmission assume that recovered individuals have a fully protected immunity. To date, there is no definite answer about whether people who recover from COVID-19 can be reinfected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the absence of a clear answer about the risk of reinfection, it is instructive to consider the possible scenarios. To study the epidemiological dynamics with the possibility of reinfection, I use a Susceptible-Exposed-Infectious-Resistant-Susceptible model with the time-varying transmission rate. I consider three different ways of modeling reinfection. The crucial feature of this study is that I explore both the difference between the reinfection and no-reinfection scenarios and how the mitigation measures affect this difference. The principal results are the following. First, the dynamics of the reinfection and no-reinfection scenarios are indistinguishable before the infection peak. Second, the mitigation measures delay not only the infection peak, but also the moment when the difference between the reinfection and no-reinfection scenarios becomes prominent. These results are robust to various modeling assumptions.

Suggested Citation

  • Malkov, Egor, 2020. "Simulation of coronavirus disease 2019 (COVID-19) scenarios with possibility of reinfection," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    • Handle: RePEc:eee:chsofr:v:139:y:2020:i:c:s0960077920306925
    DOI: 10.1016/j.chaos.2020.110296
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    References listed on IDEAS

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    1. David Berger & Kyle Herkenhoff & Chengdai Huang & Simon Mongey, 2022. "Testing and Reopening in an SEIR Model," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 43, pages 1-21, January.
    2. Andrew Atkeson, 2020. "What Will be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios," Staff Report 595, Federal Reserve Bank of Minneapolis.
    3. Hortaçsu, Ali & Liu, Jiarui & Schwieg, Timothy, 2021. "Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19," Journal of Econometrics, Elsevier, vol. 220(1), pages 106-129.
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    1. Alberto Olivares & Ernesto Staffetti, 2021. "Optimal Control Applied to Vaccination and Testing Policies for COVID-19," Mathematics, MDPI, vol. 9(23), pages 1-22, December.
    2. Sivakumaar, Vysaaly & Enayati, Shakiba & Shittu, Ekundayo, 2024. "Uncovering heterogeneous inequities induced by COVID-19 interventions: Evidence from three states in the U.S," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    3. Gabrick, Enrique C. & Protachevicz, Paulo R. & Batista, Antonio M. & Iarosz, Kelly C. & de Souza, Silvio L.T. & Almeida, Alexandre C.L. & Szezech, José D. & Mugnaine, Michele & Caldas, Iberê L., 2022. "Effect of two vaccine doses in the SEIR epidemic model using a stochastic cellular automaton," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    4. Tri Nguyen-Huu & Pierre Auger & Ali Moussaoui, 2023. "On Incidence-Dependent Management Strategies against an SEIRS Epidemic: Extinction of the Epidemic Using Allee Effect," Mathematics, MDPI, vol. 11(13), pages 1-25, June.
    5. Papageorgiou, Vasileios E. & Tsaklidis, George, 2023. "An improved epidemiological-unscented Kalman filter (hybrid SEIHCRDV-UKF) model for the prediction of COVID-19. Application on real-time data," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    6. M. Alper Çenesiz & Luís Guimarães, 2022. "COVID‐19: What if immunity wanes?," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(S1), pages 626-664, February.

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