Abstract
Tuberculosis (TB) remains a leading cause of death from a single infectious agent, with TB deaths in 2021 surpassing those from HIV/AIDS. In alignment with the WHO’s End TB strategy, South Korea launched a TB elimination plan in 2006. Recognizing the significance of age-dependent factors, we developed a comprehensive mathematical model to analyze the age-specific transmission dynamics of TB, including latent tuberculosis infection (LTBI), from 2012 to 2021. Our previous research focused on age-specific TB dynamics and the effects of various interventions on reducing active TB cases within different age groups. In this study, we enhance our model to include both short and long latent TB cases, allowing for a more detailed investigation of age-specific LTBI interventions and their impact on TB reduction. Utilizing the Markov Chain Monte Carlo (MCMC) method within a Bayesian inferential framework, we estimated model parameters that closely align with actual data with high accuracy. Our results revealed that non-elderly individuals are more responsive to transmission rates, whereas elderly individuals are more influenced by LTBI treatment. Strengthening LTBI treatment interventions significantly reduce TB infectious cases, particularly among those aged 65 and older. Detection and treatment strategies specifically targeting LTBI in the elderly population, along with primary TB treatments, are essential for TB control. The Korean government aims to reduce the TB incidence rate to below 20 cases per 100,000 by 2027. Our results indicate that this goal can be achieved by enhancing LTBI detection rates and treatment compliance levels. Specifically, our findings suggest that improving LTBI treatment interventions could lower the TB incidence to 10 cases per 100,000 by 2030. This underscores the critical importance of implementing age-specific LTBI detection and treatment interventions to effectively decrease TB incidence in South Korea.
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