My research focus is on furthering our understanding of M.tuberculosis transmission in different epidemiological contexts and characterising the role of socio-biological mechanisms that are driving disparities in exposure and susceptibility to M. tuberculosis which leads to the differential distribution of tuberculosis observed in populations. I also work with the endTB consortium (https://endtb.org/) to improve the programmatic management of drug-resistant tuberculosis in resource-limited setting through generating a robust evidence base to guide management. I am passionate about using the best available most up-to-date statistical methods to answer the research question

I would like to gain further expertise in the use of advanced formal causal inference methods as part of my research. Novel statistical methods are continuously being developed in the quantitative sciences (ecology, social science, econometrics, cognitive sciences etc.) which allow us to potentially use and combine available multi-level data in models to better describe and understand drivers of M.tuberculosis transmission within societies. These models can then be used to inform the design of new interventions and/or bespoke combination of interventions for a given setting. Targeting TB care and prevention strategies specific to the local epidemiological and societal context are much more likely to be effective than the ‘one size fits all’ approach that has been adopted to date.