ABSTRACT This study presents a comprehensive deterministic model that stratifies the population into six compartments to evaluate the epidemiological impact of media‐driven awareness programs on tuberculosis (TB) transmission dynamics. By integrating cumulative density metrics to quantify media‐induced protective behaviors, the framework rigorously explores the interplay between public health communication and disease spread. Qualitative analysis establishes the system's positivity, boundedness, and uniform persistence under specified conditions. The basic reproduction number is derived, demonstrating that the disease‐free equilibrium is locally and globally asymptotically stable when . When , the endemic equilibrium is locally and globally asymptotically stable, and the disease is uniformly persistent. Optimal control strategies, including media‐based awareness amplification () and expanded campaign coverage (), are integrated into the framework. A center manifold reduction at the threshold is carried out to compute the bifurcation coefficient and characterize the possibility of backward bifurcation induced by imperfect vaccination and waning immunity. Using Pontryagin's maximum principle, optimal control trajectories are analytically determined to minimize infection prevalence and intervention costs. Numerical simulations also support the analytical findings, revealing that dual control measures reduce peak infections by 47% and accelerate epidemic extinction by 88 days compared to uncontrolled scenarios. Sensitivity analysis identifies the transmission rate () as the most influential parameter affecting , with a PRCC value of 1. This indicates that a 10% increase in the transmission rate leads to an approximately 10% increase in . The reason for this situation might be the decline in public awareness of protection, which leads to an increase in the contact rate. The findings underscore the critical role of sustained, adaptive media interventions in disrupting TB transmission, providing actionable insights for optimizing resource allocation in public health strategies.
Wang et al. (Thu,) studied this question.