Mathematical modelling to project the impact of public health interventions targeted to individuals previously treated for tuberculosis on the trajectory of the tuberculosis epidemic and the emergence of drug-resistant diseae in Southern Africa

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Tuberculosis (TB), an air-borne infectious disease caused by Mycobacterium tuberculosis, is a major challenge to global public health. The World Health Organization estimates that in 2012, globally 8.6 million people developed TB and 1.3 million people died from the disease. Southern Africa remains the center of the global TB epidemic, where countries have experienced an unprecedented increase in TB cases over the past two decades, associated with an enormous burden of HIV/AIDS. Operational research suggests that in many settings, conventional TB control programs, which rely on passive case finding and treatment of active disease, are currently unable to substantially reduce the prevalence of TB and transmission. More aggressive public health interventions, for example population-wide screening for TB and isoniazid preventive therapy, are therefore being investigated. Given limited resources and thus far disappointing results from untargeted population-level intervention trials, the use of more targeted efforts to improve TB control may be a promising alternative. One identifiable risk group that might be considered for targeted interventions are individuals with a history of previously treated TB. These account for more than one-third of the TB burden in some high-burden settings, and thus might contribute substantially to onward transmission. Moreover, previously treated individuals may also disproportionately contribute to the spread of drug-resistant forms of TB. It is currently not known whether interventions aiming to prevent or early identify recurrent TB in previously treated individuals would be an effective or cost-effective intervention.The main goal of this project is to combine traditional epidemiological analysis and mathematical modeling to better understand the importance of previously treated TB in the context of the current TB and HIV epidemic in Southern Africa, and to project the impact of targeted control interventions. We will use an extensive source of field data from the ZAMSTAR study, a major randomized intervention trial conducted in 24 communities in Zambia and South Africa, to study previously treated tuberculosis in high-burden settings. We further aim to develop a novel, population-based dynamic compartmental mathematical model of the TB epidemic that incorporates history of previously treated TB, HIV co-infection and drug-resistant TB in the population. The model will be calibrated to TB data from 9 African countries. We aim to use the model to project the impact of various control interventions targeted to previously treated individuals on the trajectory of the TB epidemic and the emergence of drug-resistance in different settings in Southern Africa. The research project seeks to make an important contribution to current knowledge about the complex dynamics of TB and HIV epidemics and to inform the development of novel tools and strategies for TB and drug-resistant TB control in Southern Africa and elsewhere.