Using mathematical modelling to fill knowledge gaps in tuberculosis epidemiology

I am part of a world-renowned TB modelling team having unique dual expertise in research and programmatic guidance for TB control. We combine state-of-the-art modelling methods with the most up-to-date epidemiological data to produce realistic simulations of TB epidemics in many settings. The countries where our models have been applied include Papua New Guinea, Uzbekistan, South Africa, India, China, Fiji, the Philippines, Bulgaria, Bhutan, Mongolia, Vietnam and the Marshall Islands. These works are supported by the Global Fund to Fight AIDS, TB and Malaria, the World Health Organization and NHMRC. While our previous works employed compartmental models governed by ordinary differential equations, our team has recently developed other sophisticated tools including agent-based models (Ragonnet et al, BMC Medicine 2019) and semi-mechanistic models. Such models allow highly realistic simulations of M.tb transmission and incorporate important heterogeneities which are known to be critical in TB epidemiology. This project will aim to characterise poorly known and yet fundamental aspects of TB including: - the detailed age-specific profile of infectiousness, - the waning profile of BCG vaccine efficacy over time, - the gender-based differences in TB epidemiology. The student will have access to real-world data (e.g. age-specific and gender-specific disease prevalence) that will be used to calibrate and validate the models. The student should be able to code in Python and have a solid knowledge of mathematics.