A Four-Strain Model of Drug-Resistant Tuberculosis in the United States



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Tuberculosis (TB) is a disease of global epidemiological concern. It is estimated that one-third of the world’s population (including 11 million people in the US) are currently infected with TB. Insufficient or irresponsible treatment of TB with antibiotics can select for drug-resistant bacteria, which are much more difficult to treat. In particular, Multi-Drug-Resistant (MDR) and Extensively-Drug-Resistant (XDR) cases of TB require treatment regimens that are expensive, long lasting, toxic, and often unsuccessful. Despite the importance of drug-resistance to understanding the current state of TB epidemiology, many published models of TB do not take resistance into account. In this project, a compartmental mathematical model of TB epidemiology is presented. The model consists of four strains of TB, including one drug-susceptible strain, two strains that are each resistant to a single drug, and one MDR strain. This model fits accurately to several sets of relevant data collected by the CDC in the years 2000-2013, improving upon some previous predictions for the transmission of TB in the US. It also predicts the efficacy of various interventions with the goal of reducing the incidence of TB and MDR TB in particular. The effects of interventions on TB epidemiology are modeled by modifying relevant parameter values starting at the year 2015 and comparing the projected incidence of TB. The most promising interventions for reducing TB and MDR TB incidence are decreasing treatment time, decreasing the potential for new infections via quarantine, and decreasing LTBI cases in the immigrant population. However, complete elimination of TB is not feasible for the foreseeable future.



Epidemiology, Mathematical Modeling, Tuberculosis, Disease Modeling, Drug-Resistance