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Commun. Math. Biol. Neurosci. 2023 (2023), Article ID 5

A SEIQRD epidemic model to study the dynamics of COVID-19 disease

Isnani Darti, Trisilowati -, Maya Rayungsari, Raqqasyi Rahmatullah Musafir, Agus Suryanto

Abstract

In this paper, we propose a COVID-19 epidemic model with quarantine class. The model contains 6 sub-populations, namely the susceptible (S), exposed (E), infected (I), quarantined (Q), recovered (R), and death (D) sub-populations. For the proposed model, we show the existence, uniqueness, non-negativity, and boundedness of solution. We obtain two equilibrium points, namely the disease-free equilibrium (DFE) point and the endemic equilibrium (EE) point. Applying the next generation matrix, we get the basic reproduction number (R0). It is found that R0 is inversely proportional to the quarantine rate as well as to the recovery rate of infected subpopulation. The DFE point always exists and if R0 < 1 then the DFE point is asymptotically stable, both locally and globally. On the other hand, if R0 > 1 then there exists an EE point, which is globally asymptotically stable. Here, there occurs a forward bifurcation driven by R0. The dynamical properties of the proposed model have been verified our numerical simulations.

Published
16 January 2023
Volume
2023 (2023)
License
CC BY 3.0

Copyright © 2023 Isnani Darti, Trisilowati -, Maya Rayungsari, Raqqasyi Rahmatullah Musafir, Agus Suryanto. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Cite this article

I. Darti, T. -, M. Rayungsari, R. R. Musafir, A. Suryanto, A SEIQRD epidemic model to study the dynamics of COVID-19 disease, Commun. Math. Biol. Neurosci. 2023 (2023), 5. https://doi.org/10.28919/cmbn/7822

BibTeX
@article{Darti2023,
  author  = {Darti, Isnani and -, Trisilowati and Rayungsari, Maya and Musafir, Raqqasyi Rahmatullah and Suryanto, Agus},
  title   = {A SEIQRD epidemic model to study the dynamics of COVID-19 disease},
  journal = {Commun. Math. Biol. Neurosci.},
  volume  = {2023},
  year    = {2023},
  pages   = {5},
  doi     = {10.28919/cmbn/7822}
}