The emergence of an epidemic of multidrug resistant (MDR) and extensively drug-resistant (XDR) TB in South Africa threatens to undermine the advances made in the antiretroviral therapy (ART) rollout for HIV. While contact investigations for household members of individuals with MDR or XDR TB holds practical appeal, the potential benefit of this strategy in the control of drug-resistant tuberculosis in a high TB prevalence setting is not known. Mathematical models evaluating the control of TB have not accounted for the realistic contact structure in respiratory transmission, precluding the evaluation of contact investigation strategies. This K01 application proposes to draw upon age-structured data on close contact rates from a South African in order to model tuberculosis transmission. Using this framework, the following specific aims are proposed: 1) To design and populate a network model of tuberculosis natural history and transmission and in a high HIV prevalence setting using empirical social contact data; 2)To evaluate the effect of social contact rates and network structure on the projections of tuberculosis incidence and on the impact of enhanced case-finding and treatment success; 3) To project the clinical impact and cost-effectiveness of tuberculosis screening and provision of preventive therapy for contacts of individuals with multidrug-resistant and extensively drug- resistant tuberculosis in a high HIV prevalence setting. This work will inform public health policy as well as clinical trials for development of chemoprophylaxis for MDR/XDR TB, which is a key aim of the NIAID Research Agenda for Multidrug-Resistant and Extensively Drug-Resistant Tuberculosis. The candidate, Jason Andrews, M.D., S.M.,DTM&H is a postdoctoral fellow in infectious diseases at Massachusetts General Hospital and will benefit from the mentorship of investigators with extensive expertise in mathematical modeling, network analysis, and statistical inference. This research will be conducted in collaboration with investigators at the Desmond Tutu HIV Centre at the University of Cape Town, where extensive clinical and community-based data on HIV and TB natural history and epidemiology are available. The Candidate will gain expertise in advanced methods for mathematical modeling, network analysis and statistical inference of dynamic systems through coursework at the Harvard School of Public Health and the Applied Mathematics Department at the Harvard School of Engineering and Applied Sciences. The proposed training and research experience will provide the foundation for a career as an independent investigator.
The rise of multidrug-resistant and extensively drug-resistant tuberculosis in high HIV prevalence settings threatens to undermine the success of the antiretroviral therapy rollout in improving life expectancy for people living with HIV. By developing models for tuberculosis transmission that incorporate the social contact structure in sub-Saharan African townships, this research will better elucidate how tuberculosis spreads and will project the impact of interventions to control drug-resistant tuberculosis.
