Controlling the transmission dynamics of COVID-19

Stephen Edward Moore, Eric Okyere

Abstract


The outbreak of COVID-19 caused by SARS-CoV-2 in Wuhan and other cities in China in 2019 has become a global pandemic as declared by the World Health Organization (WHO) in the first quarter of 2020. The delay in diagnosis, limited hospital resources and other treatment resources led to the rapid spread of COVID19. Optimal control dynamical models with time-dependent functions are very powerful mathematical modeling tools to investigate the transmission of infectious diseases. In this study, we have introduced and studied a new mathematical model for COVID-19 disease using personal protection, hospitalization and treatment of infectious individuals with early diagnosis, hospitalization and treatment of infectious individuals with delayed diagnosis and spraying of the environment as time-dependent control functions. This new non-autonomous deterministic epidemic model for the 2019 coronavirus disease is an extension of a recently constructed and analyzed data-driven non-optimal control model. We investigated three control strategies for our model problem. From the numerical illustrations of the various control strategies, we realized that the third strategy, which captures all the four time-dependent control functions, yields better results.

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Published: 2022-01-17

How to Cite this Article:

Stephen Edward Moore, Eric Okyere, Controlling the transmission dynamics of COVID-19, Commun. Math. Biol. Neurosci., 2022 (2022), Article ID 6

Copyright © 2022 Stephen Edward Moore, Eric Okyere. 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.

Commun. Math. Biol. Neurosci.

ISSN 2052-2541

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