Estimating TB reactivation rates following exposure

This project aims to better define rates of reactivation from the point of initial exposure/infection to the later development of active TB. The research hypothesis is that specific “profiles” or “patterns” of reactivation exist, and are strongly influenced by age at exposure, age (at reactivation to TB) and BCG vaccination status. By contrast, the traditional understanding is that the lifetime risk of TB is 5-10% following infection, with half of that risk accruing in the earliest years following exposure. We believe this to be a considerable oversimplification of the true risk profiles, with major implications for the understanding of TB epidemiology, the impact of control interventions and the construction of transmission dynamic models. My team has previously explored these questions through a series of papers that have begun to elucidate the interacting effects of age at exposure, age and BCG vaccination status. This has included reconstructing survival analyses using data from the Victorian Tuberculosis Program and reviewing historical datasets relating to this question, resulting in a series of four peer reviewed journal articles. We have also set up a data repository in a secure location at Monash through which we can receive similar international datasets and are in the early phase of receiving such overseas data. In this project, the PhD candidate would analyse the data from Victoria and overseas using statistical methods and machine learning algorithms to define the true underlying profiles of reactivation. This project would suit applicants with an interest in developing skills in applied computational epidemiology. Key background would include skills from the fields of statistics, data science and epidemiology, as well as a strong ability to code in languages such as Python or R.