Optimal control of a two-strain tuberculosis-HIV/AIDS co-infection model
Agusto, F.B., and Adekunle, A.I. (2014) Optimal control of a two-strain tuberculosis-HIV/AIDS co-infection model. BioSystems, 119. pp. 20-44.
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Abstract
Tuberculosis is a bacterial disease caused by Mycobacterium tuberculosis (TB). The risk for TB infection greatly increases with HIV infection; TB disease occurs in 7–10% of patients with HIV infection each year, increasing the potential for transmission of drug-resistant Mycobacterium tuberculosis strains. In this paper a deterministic model is presented and studied for the transmission of TB-HIV/AIDS co-infection. Optimal control theory is then applied to investigate optimal strategies for controlling the spread of the disease using treatment of infected individuals with TB as the system control variables. Various combination strategies were examined so as to investigate the impact of the controls on the spread of the disease. And incremental cost-effectiveness ratio (ICER) was used to investigate the cost effectiveness of all the control strategies. Our results show that the implementation of the combination strategy involving the prevention of treatment failure in drug-sensitive TB infectious individuals and the treatment of individuals with drug-resistant TB is the most cost-effective control strategy. Similar results were obtained with different objective functionals involving the minimization of the number of individuals with drug-sensitive TB-only and drug-resistant TB-only with the efforts involved in applying the control.
| Item ID: | 51083 |
|---|---|
| Item Type: | Article (Research - C1) |
| ISSN: | 1872-8324 |
| Keywords: | tuberculosis; drug resistant strain; HIV/AIDS; stability; optimality system; cost effectiveness |
| Date Deposited: | 09 Oct 2017 04:20 |
| FoR Codes: | 01 MATHEMATICAL SCIENCES > 0102 Applied Mathematics > 010204 Dynamical Systems in Applications @ 100% |
| SEO Codes: | 92 HEALTH > 9201 Clinical Health (Organs, Diseases and Abnormal Conditions) > 920109 Infectious Diseases @ 100% |
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