PROJECT SUMMARY/ABSTRACT Tuberculosis (TB) remains a leading cause of global mortality. Treatment is efficacious and low cost for most people, but clinic-based (“passive”) case detection leaves millions of people with TB undiagnosed every year. To accelerate progress against TB, we must bring detection and prevention to communities – but community- based interventions, such as systematic screening and TB preventive treatment (TPT), are resource intensive at scale. Evidence on how to target community-based TB interventions for greatest impact and efficiency is therefore urgently needed. Novel data sources – including aggregated cell phone records, genomic sequencing, and empiric estimates of cost – are emerging as critical tools for understanding and fighting TB in high-burden settings. Scientists who can analyze these data using rigorous and reproducible techniques while applying a health policy lens will be essential to our future success against the world’s leading infectious killer. The goal of this project is to identify ways to make community-level TB screening and prevention more impactful and cost-effective by better characterizing the spatial dynamics of TB and Mycobacterium tuberculosis (Mtb) transmission. My training aims include: (1) develop expertise in spatial epidemiology and the analysis of mobility data; (2) obtain training in molecular epidemiology; and (3) gain skills to link these data sources to mathematical models. These aims link to three research aims: (1) refine estimates of subnational TB prevalence by synthesizing emerging sources of data; (2) use genomic data to identify key populations among whom Mtb transmission is concentrated; and (3) project the impact and cost-effectiveness of targeted TB screening and prevention approaches. This research will leverage both aggregated cell phone mobility data and genomic, spatial, mobility, and epidemiologic data from three studies in Uganda and South Africa with unique designs that lend themselves well toward describing the spatial scale of Mtb transmission. To achieve success as an independent investigator, I also plan to obtain experience with field-based epidemiological studies and gain familiarity with the clinical management of TB in high-burden settings. I am a junior faculty member in the Division of Infectious Diseases at the Johns Hopkins School of Medicine with a quantitative background in infectious disease modeling and health policy. My long-term career goal is to become an independent researcher who informs more evidence-based TB policymaking by integrating diverse sources of data into mathematical models. During this award period, I will be mentored by a team whose expertise spans TB epidemiology and clinical care, molecular epidemiology, infectious disease dynamics, human mobility, and mechanistic and statistical disease modeling. Collectively, this research and career development plan will provide a pathway to a career as an independent investigator situated at th...