HIV–TB co-infection treatment control using multi-objective optimized sliding mode

• Main Authors: S. Hadipour Lakmesari, M.J. Mahmoodabadi, S. Hadipour
• Format: Article
• Language: English
• Published: Elsevier 2020-01-01
• Series: Informatics in Medicine Unlocked
• Subjects: HIV-TB co-Infection; Human immunodeficiency virus; Tuberculosis; Sliding mode control; Multi-objective genetic algorithm
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352914820300289

Mycobacterium tuberculosis (MTB) and Human immunodeficiency virus (HIV) are two causes of infection which threaten human health over recent decades. There is abundant evidence indicating that these diseases are related to each other. People living with HIV have a 30-fold increase in tuberculosis infection compared to healthy people. In recent years, medical researchers and other specialists including engineers and basic scientists have tried to propose treatments for dangerous diseases by means of mathematical simulation. In this paper, a mathematical model of the HIV-TB co-infection individuals is regarded in order to represent different characteristics of such people. In the next step, a sliding mode controller is implemented on first and second HIV treatment rates to minimize the Total Burden resulted from the co-infection. Moreover, the multi-objective genetic algorithm (MOGA) is used to optimize the sliding mode controller coefficients. The MOGA is accurate and resilient and its Pareto front has a reasonable variety. In this method, designers acceptably use defeated points with more than one objective function and are also able to choose appropriate solutions according to their preferences. The results suggest that disease deaths, new infections, and immune reconstitution inflammatory syndrome cases play an important role in the problem.