Mathematical model for the control of HIV/TB coinfection incorporating dual stigma

Abstract

The co-epidemics of HIV and Tuberculosis, intensified by dual stigma, pose major challenges to effective disease control. Understanding the interaction between coinfection dynamics and stigma is essential for designing targeted interventions. This study develops a deterministic mathematical model incorporating dual stigma and control strategies to analyze HIV/TB transmission dynamics. Separate analyses of HIV-only, TB-only, and coinfection sub-models are conducted. Numerical simulations assess the impact of treatment and stigma-reduction interventions, while sensitivity analysis and contour plots evaluate the influence of key parameters on the basic reproduction numbers. Results indicate that increased treatment rates and awareness campaigns significantly reduce infection prevalence and stigma, thereby lowering transmission. Stigma is shown to exacerbate disease spread by limiting treatment uptake and increasing susceptibility. Sensitivity analysis identifies treatment efficacy and awareness-related parameters as the most influential factors in reducing reproduction numbers. Overall, integrating medical treatment with stigma-reduction strategies is highly effective in controlling HIV/TB coinfection. These findings highlight the importance of combined biomedical and social interventions and provide practical insights for policymakers.