Modeling the Impact of Treatment During an Ongoing Tuberculosis Outbreak

In response to a significant tuberculosis outbreak in Wyandotte and Johnson Counties, Kansas, this study presents a compartmental model of ordinary differential equations to evaluate the impact of standard antibiotic treatment. The model incorporates latent, active, and treated disease states. Parameter values were informed by epidemiological data and uncertain parameter value ranges were explored systematically through uncertainty analysis using constrained Latin hypercube sampling. Cumulative infections and deaths, and the basic reproduction number, were computed over a five-year simulation period. Sensitivity analyses using partial rank correlation coefficients identified symptomatic treatment rate and transmission rate as primary drivers of cumulative infections and deaths. Model results indicate that treatment significantly reduces both infections and mortality, but that relying solely on treatment has high associated costs underscoring the importance of integrating transmission mitigation measures alongside treatment. This modeling framework offers valuable insight for public health policy, especially in managing resource allocation during tuberculosis outbreaks.