Hypercubic dynamical system modelling of first-line drug resistance in mycobacterium tuberculosis in Tanzania: A data-driven approach with treatment intervention

Abstract Tuberculosis (TB) remains a significant global health threat, despite extensive efforts to combat the disease. A major challenge in TB control is the emergence of complications, particularly those arising from drug resistance. The increasing transmission of resistant strains has rendered first-line treatments less effective against Mycobacterium tuberculosis (MTB), complicating patient management and increasing public health burden. In this study, we investigated the dynamics of the first-line anti-TB drugs isoniazid and rifampicin, which are critical for effective treatment. Using epidemiological data collected in Tanzania, we developed a mathematical model to simulate the transmission and resistance patterns of MTB. All parameters were estimated from the data, and local and global sensitivity analyses were performed to identify the most influential parameters on drug resistance. Furthermore, phase portraits were used to analyse the interactions between the subpopulations, which provided patterns that need attention in combating the drug resistance for the first-line MTB drugs. In addition, simulations of various intervention scenarios were carried out to explore the potential impact of different strategies to mitigate resistance dynamics. The findings provided valuable insights into the dynamics of drug resistance and offered evidence based recommendations for optimising TB treatment protocols. The study underscored the urgent need for innovative approaches to address the growing challenge of drug-resistant TB and furthermore highlights the importance of data-driven modelling in informing public health policies.