Speakers
Description
Tuberculosis (TB) remains a worldwide health challenge. Adherence to TB treatment is usually compromised due to patients' concerns related to social stigma, economic constraints, and the demanding nature of long and closely monitored treatment regimens. This study introduces a novel Susceptible-Latent-Infected-Treated-Recovered-Failed (SLITRF) compartmental model that integrates dynamic game theory to optimize treatment adherence strategies. We formulate the treatment-adherence dynamics as a non-cooperative game between patients and healthcare providers. We further derive Nash and Stackelberg equilibria to balance treatment efficacy against toxicity costs and external risks. Disease-free equilibrium (DFE) and endemic equilibrium (EE) are explicitly computed, with the basic reproduction number $\mathcal{R}_0$ obtained via the next-generation matrix method. The stability analysis of DFE is performed for different conditions on $\mathcal{R}_0$. This framework of a game-theoretic epidemiological model provides insights for TB treatment management.
