Mathematical analysis of a methicillin-resistant Staphylococcus aureus model in hospitals and the community

Josiah Mushanyu, Farai Nyabadza

Abstract


In this paper, we study a methicillin-resistant Staphylococcus aureus epidemic model taking into account both hospitals’ and the general community dynamics. The model is designed to track the long-term dynamics of methicillin-resistant Staphylococcus aureus infections. Mathematical analysis of the developed model is carried out. The MRSA-free equilibrium Moa and the model reproduction number R0 are established. Numerical simulations are performed using previously published data from relevant scientific literature. It is shown that both the MRSA-free equilibrium and the MRSA-persistent equilibrium are locally asymptotically stable when R0<1 and R0>1, respectively. Numerical simulations are also conducted to ascertain the effects of variations in key parameter values on specific compartments: (i) hospitalized individuals exclusively, (ii) the general community exclusively, or (iii) both hospitalized individuals and the general community concurrently. It is shown that 1% increase in the values of βh and βc corresponds to approximately 27.1% and 8.3% increase in the value of R0, respectively. On the other hand, a 0.1% increase in the values of ε and ψ, and a 0.25% increase in the value of δ corresponds to approximately 0.53%, 0.498% and 0.267% decrease in the value of R0, respectively. The findings suggest the need for policymakers to implement robust measures aimed at minimizing infection transmission between both infected and susceptible individuals, encompassing both nosocomial environments and the wider community.

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Published: 2024-09-12

How to Cite this Article:

Josiah Mushanyu, Farai Nyabadza, Mathematical analysis of a methicillin-resistant Staphylococcus aureus model in hospitals and the community, Commun. Math. Biol. Neurosci., 2024 (2024), Article ID 94

Copyright © 2024 Josiah Mushanyu, Farai Nyabadza. 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.

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