Sensitivity analysis and optimal countermeasures control of model of the spread of COVID-19 co-infection with HIV/AIDS

Jonner Nainggolan, Joko Harianto, Moch. Fandi Ansori

Abstract


This paper analyzes and examines the optimal control in the co-infection of COVID-19 with HIV/AIDS by providing preventive and treatment control measures. The population is divided into eight subpopulations. The preventive control of COVID-19 is denoted by u1. The preventive control of HIV/AIDS is denoted by u2. The treatment control of COVID-19 is denoted by u3, and the treatment control of COVID-19 for the subpopulation co-infected with HIV/AIDS is denoted by u4. Based on the model analysis, non-endemic and endemic equilibrium points are obtained, along with the basic reproduction number of the COVID-19, HIV/AIDS, and COVID-19-HIV/AIDS sub-models. Numerical simulations reveal that using preventive control u1 is more effective in reducing the spread of COVID-19 compared to u3 or u4, both individually and together. Preventive control u2 is more effective in controlling the spread of HIV/AIDS compared to the absence of control. The sensitivity analysis of parameter identifies parameters that significantly affect the reduction or increase in the spread of COVID-19-HIV/AIDS co-infection. We found that in order to reduce the co-infection’s spread, we should pay attention to the reducing the contact rate of HIV/AIDS patients or increasing their treatment rate.

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Published: 2023-09-12

How to Cite this Article:

Jonner Nainggolan, Joko Harianto, Moch. Fandi Ansori, Sensitivity analysis and optimal countermeasures control of model of the spread of COVID-19 co-infection with HIV/AIDS, Commun. Math. Biol. Neurosci., 2023 (2023), Article ID 96

Copyright © 2023 Jonner Nainggolan, Joko Harianto, Moch. Fandi Ansori. 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.

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