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Commun. Math. Biol. Neurosci. 2020 (2020), Article ID 24

A mathematical modeling with optimal control strategy of transmission of COVID-19 pandemic virus

Abdelfatah Kouidere, Bouchaib Khajji, Amine El Bhih, Omar Balatif, Mostafa Rachik

Abstract

In this paper, we propose a mathematical modeling that describe the dynamics of transmission of the novel Coronavirus 2019 (COVID-19), between potential people and infected people without symptoms, and those infected people with symptoms and then people with serious complications, as well as those under health surveillance and quarantine, in addition to people who recovered from the virus.

In addition, we propose an optimal strategy by carrying out awareness campaigns for citizens with practical measures to reduce the spread of the virus, and diagnosis and surveillance of airports and the quarantine of infected people.

Pontryagin’s maximum principle is used to characterize the optimal controls, and the optimality system is solved by an iterative method. Finally, some numerical simulations are performed to verify the theoretical analysis using Matlab.

Published
11 May 2020
Volume
2020 (2020)
License
CC BY 3.0

Copyright © 2020 Abdelfatah Kouidere, Bouchaib Khajji, Amine El Bhih, Omar Balatif, Mostafa Rachik. 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

A. Kouidere, B. Khajji, A. El Bhih, O. Balatif, M. Rachik, A mathematical modeling with optimal control strategy of transmission of COVID-19 pandemic virus, Commun. Math. Biol. Neurosci. 2020 (2020), 24. https://doi.org/10.28919/cmbn/4599

BibTeX
@article{Kouidere2020,
  author  = {Kouidere, Abdelfatah and Khajji, Bouchaib and El Bhih, Amine and Balatif, Omar and Rachik, Mostafa},
  title   = {A mathematical modeling with optimal control strategy of transmission of COVID-19 pandemic virus},
  journal = {Commun. Math. Biol. Neurosci.},
  volume  = {2020},
  year    = {2020},
  pages   = {24},
  doi     = {10.28919/cmbn/4599}
}