Skip to content
Communications in Mathematical Biology and Neuroscience ISSN 2052-2541

Archives › Volume 2020 (2020)

Commun. Math. Biol. Neurosci. 2020 (2020), Article ID 71

The hidden role of the pre-symptomatic individuals in the transmission dynamics of COVID-19

Nasser Al-Salti, Fatma Al-Musalhi, Maryam Al-Yahyai, Ibrahim M. Elmojtaba

PDF (327.1 KB) https://doi.org/10.28919/cmbn/4990

Abstract

In this paper, a mathematical model with four different routes of transmission, namely, asymptomatic, pre-symptomatic, symptomatic and environmental transmissions, has been proposed and analyzed to investigate the role of pre-symptomatic individuals in the transmission dynamics of COVID-19 outbreak. Using the next generation matrix method, the basic reproduction number R0 has been derived and then sensitivity analysis of the proposed model is presented. Existence and stability analysis of disease free and endemic equilibrium points have been discussed. Numerical simulations to demonstrate the effect of some model parameters related to presymptomatic transmission on the disease transmission dynamics have been carried out.

Published
16 October 2020
Volume
2020 (2020)
License
CC BY 3.0

Copyright © 2020 Nasser Al-Salti, Fatma Al-Musalhi, Maryam Al-Yahyai, Ibrahim M. Elmojtaba. 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

N. Al-Salti, F. Al-Musalhi, M. Al-Yahyai, I. M. Elmojtaba, The hidden role of the pre-symptomatic individuals in the transmission dynamics of COVID-19, Commun. Math. Biol. Neurosci. 2020 (2020), 71. https://doi.org/10.28919/cmbn/4990

BibTeX
@article{Al-Salti2020,
  author  = {Al-Salti, Nasser and Al-Musalhi, Fatma and Al-Yahyai, Maryam and Elmojtaba, Ibrahim M.},
  title   = {The hidden role of the pre-symptomatic individuals in the transmission dynamics of COVID-19},
  journal = {Commun. Math. Biol. Neurosci.},
  volume  = {2020},
  year    = {2020},
  pages   = {71},
  doi     = {10.28919/cmbn/4990}
}