Vaccines against bacterial pathogens have been proposed as a means to combat antibiotic resistant infections (e.g. Lipsitch and Siber, 2016; Atkins et al. 2018). For example, by reducing the total burden of pneumococcal infections, pneumococcal conjugate vaccines would also reduce the number of resistant pneumococcal infections. However, the exact impact of these vaccines are determined by the epidemiological and evolutionary dynamics of the circulating pathogens (Davies et al., 2021). Specifically, while the spatial structure of antibiotic use, the pathogen diversity and the within-host dynamics of infection can all determine the frequency of antibiotic resistant infections, the relative importance of these mechanisms is unknown. Therefore, understanding the impact of vaccines to prevent antibiotic resistance hinges on quantifying these dynamics.

Phylogenetic analysis provides a tool to quantify infectious disease dynamics by leveraging the information contained in genetic sequence data to infer epidemic spread. This project will use rich genetic and complementary epidemiological data from a multi-year cluster randomized trial for a pneumococcal conjugate vaccine (PCV) in Vietnam to help elucidate the underlying dynamics of S. pneumoniae, a major cause of childhood pneumonia. Using deep sequence genetic data across four years of sampling in a high antibiotic use setting, these data will provide unparalleled insight into the dynamics of drug resistance and the impact of pneumococcal conjugate vaccines on drug resistant infections.

The project will use an interdisciplinary combination of genetic sequence data analysis, epidemiology, and phylogenetic analysis. The candidate will develop their quantitative skills using phylogenetic, statistical and mathematical analysis. The student will develop or extend their programming expertise in languages, such as R or Python. Emphasis will be placed on developing and sharing code for the wider scientific community through platforms such as GitHub.