Mathematical Modelling and Applications

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Mathematical Study of the Optimal Control of COVID-19 Transmission Amongst the Vaccinated Individuals

Center for Disease control, informs that it takes two weeks after one is fully vaccinated for the body to build protection (immunity) against the virus that causes COVID-19. Moreover, no vaccine is hundred percent effective and that includes the COVID-19 vaccines. This implies that one can still contact and spread the virus for some days after getting vaccinated. In this paper, we formulated a model for COVID-19 transmission dynamics amongst the vaccinated individuals using differential equations. We analyzed all the parameters that are responsible for the disease spread and showed the effect of other social control measures, like the use of face masks in the public, on the spread of the virus. Numerical values of these parameters were derived from some acknowledged literatures, some calculated with the data from other literatures and others judiciously estimated. The disease reproduction number R0 was obtained and found that the disease will only spread if its value exceeds one. Numerical simulation was carried out on the model, using MATLAB to show the dynamics in the different compartments and the effect of these other social control measures on the disease spread among the vaccinated individuals. The result showed that in the absence of other social control measures, almost all the vaccinated persons will be infected and will be able to infect others especially within few days of receiving the COVID-19 vaccine.

Mathematical Modelling, COVD-19 Transmission, COVID-19 Vaccines, Numerical Simulation, Reproduction Number

Christopher Chukwuma Asogwa, Stephen Ekwueme Aniaku, Emmanuel Chukwudi Mbah. (2022). Mathematical Study of the Optimal Control of COVID-19 Transmission Amongst the Vaccinated Individuals. Mathematical Modelling and Applications, 7(1), 26-32.

Copyright © 2022 Authors retain the copyright of this article.
This article 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.

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