tetano
Editor, Senior Moderator
Chaos Solitons Fractals
. 2020 Oct;139:110296.
doi: 10.1016/j.chaos.2020.110296. Epub 2020 Sep 18.
Simulation of coronavirus disease 2019 (COVID-19) scenarios with possibility of reinfection
Egor Malkov[SUP] 1 2 [/SUP]
Affiliations
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.
Keywords: COVID-19; Epidemiological dynamics; Mitigation; Reinfection; SEIRS model.
. 2020 Oct;139:110296.
doi: 10.1016/j.chaos.2020.110296. Epub 2020 Sep 18.
Simulation of coronavirus disease 2019 (COVID-19) scenarios with possibility of reinfection
Egor Malkov[SUP] 1 2 [/SUP]
Affiliations
- PMID: 32982082
- PMCID: PMC7500883
- DOI: 10.1016/j.chaos.2020.110296
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.
Keywords: COVID-19; Epidemiological dynamics; Mitigation; Reinfection; SEIRS model.