Time-delayed modelling of the COVID-19 dynamics with a convex incidence rate.

34D20 37N25 39A60 92B05 COVID-19 Convex incidence rate Delay differential equation SEIR epidemic model Stability

Journal

Informatics in medicine unlocked
ISSN: 2352-9148
Titre abrégé: Inform Med Unlocked
Pays: England
ID NLM: 101718051

Informations de publication

Date de publication:
2022
Historique:
received: 15 08 2022
revised: 31 10 2022
accepted: 01 11 2022
entrez: 21 11 2022
pubmed: 22 11 2022
medline: 22 11 2022
Statut: ppublish

Résumé

COVID-19 pandemic represents an unprecedented global health crisis which has an enormous impact on the world population and economy. Many scientists and researchers have combined efforts to develop an approach to tackle this crisis and as a result, researchers have developed several approaches for understanding the COVID-19 transmission dynamics and the way of mitigating its effect. The implementation of a mathematical model has proven helpful in further understanding the behaviour which has helped the policymaker in adopting the best policy necessary for reducing the spread. Most models are based on a system of equations which assume an instantaneous change in the transmission dynamics. However, it is believed that SARS-COV-2 have an incubation period before the tendency of transmission. Therefore, to capture the dynamics adequately, there would be a need for the inclusion of delay parameters which will account for the delay before an exposed individual could become infected. Hence, in this paper, we investigate the SEIR epidemic model with a convex incidence rate incorporated with a time delay. We first discussed the epidemic model as a form of a classical ordinary differential equation and then the inclusion of a delay to represent the period in which the susceptible and exposed individuals became infectious. Secondly, we identify the disease-free together with the endemic equilibrium state and examine their stability by adopting the delay differential equation stability theory. Thereafter, we carried out numerical simulations with suitable parameters choice to illustrate the theoretical result of the system and for a better understanding of the model dynamics. We also vary the length of the delay to illustrate the changes in the model as the delay parameters change which enables us to further gain an insight into the effect of the included delay in a dynamical system. The result confirms that the inclusion of delay destabilises the system and it forces the system to exhibit an oscillatory behaviour which leads to a periodic solution and it further helps us to gain more insight into the transmission dynamics of the disease and strategy to reduce the risk of infection.

Identifiants

pubmed: 36406926
doi: 10.1016/j.imu.2022.101124
pii: S2352-9148(22)00261-1
pmc: PMC9652120
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

101124

Informations de copyright

© 2022 The Author(s).

Déclaration de conflit d'intérêts

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Auteurs

Oluwatosin Babasola (O)

Department of Mathematical Sciences, University of Bath, BA2 7AY, UK.

Oshinubi Kayode (O)

Universite Grenoble Alpes, France.

Olumuyiwa James Peter (OJ)

Department of Mathematical and Computer Sciences, University of Medical Sciences Ondo City, Nigeria.
Department of Epidemiology and Biostatistics, University of Medical Sciences Ondo City, Nigeria.

Faithful Chiagoziem Onwuegbuche (FC)

SFI Center for Research Training in Machine Learning, University College Dublin, Ireland.

Festus Abiodun Oguntolu (FA)

Department of Mathematics, Federal University of Technology, Minna Niger State, Nigeria.

Classifications MeSH