Malkov2020 - SEIRS model of COVID-19 transmission with time-varying R values and reinfection

  public model
Model Identifier
BIOMD0000000980
Short description
Epidemiological models of COVID-19 transmission assume that recovered individuals have a fully pro- tected 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 in- distinguishable 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.
Format
SBML (L3V1)
Related Publication
  • Simulation of coronavirus disease 2019 (COVID-19) scenarios with possibility of reinfection.
  • Malkov E
  • Chaos, solitons, and fractals , 10/ 2020 , Volume 139 , pages: 110296 , PubMed ID: 32982082
  • Department of Economics, University of Minnesota, 1925 Fourth Street South, Minneapolis, MN 55455, USA.
  • 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.
Contributors
Kausthubh Ramachandran

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Model files

Malkov2020.xml SBML L3V1 file of SEIRS model of COVID-19 transmission with time-varying R values and reinfection 38.16 KB Preview | Download

Additional files

Malkov2020.omex COMBINE archive of SEIRS model of COVID-19 transmission with time-varying R values and reinfection 12.22 KB Preview | Download
Malkov2020.cps COPASI 4.29 (Build 228) file of SEIRS model of COVID-19 transmission with time-varying R values and reinfection 62.74 KB Preview | Download
Malkov2020.sedml SED-ML file of SEIRS model of COVID-19 transmission with time-varying R values and reinfection 4.04 KB Preview | Download

  • Model originally submitted by : Kausthubh Ramachandran
  • Submitted: Dec 7, 2020 7:15:56 AM
  • Last Modified: Dec 7, 2020 7:15:56 AM
Revisions
  • Version: 3 public model Download this version
    • Submitted on: Dec 7, 2020 7:15:56 AM
    • Submitted by: Kausthubh Ramachandran
    • With comment: Automatically added model identifier BIOMD0000000980
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Curator's comment:
(added: 07 Dec 2020, 07:15:24, updated: 07 Dec 2020, 07:15:24)
Fig 2(c) and Fig 2(f) are reproduced here with all parameters as mentioned in the manuscript