Supervisory team
LSHTM
- Lead: Dr. Katherine Atkins (Katherine.Atkins@lshtm.ac.uk Faculty of Epidemiology and Population Health)
- Dr. Stéphane Hué (Stephane.Hue@lshtm.ac.uk)
Nagasaki University
- Prof. Michiko Toizumi (toizumi@nagasaki-u.ac.jp)
- Prof. Lay Myint Yoshida (lmyoshi@nagasaki-u.ac.jp)
Project
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.
To analyse these data, the student will use phylogenetic analysis, a powerful tool to quantify infectious disease dynamics by leveraging the information contained in genetic sequence data to infer epidemic spread. Specifically, the student will quantify the transmission of resistant strains of S. pneumoniae between age groups and locales within the city of Nha Trang, Vietnam. They will evaluate how this transmission changes between unvaccinated and vaccinated trial clusters.
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.
This project will provide important information to understand the effects of PCVs in a high burden and high antibiotic use setting. The student will be embedded within a larger team assessing the wider impact of this vaccine and the epidemiology of S. pneumoniae.
References
Lipsitch M and Siber G (2016) doi: 10.1128/mBio.00428-16
Davies NG, Flasche S, Jit M, Atkins KE (2021) doi: 10.1126/scitranslmed.aaz8690
Dewé TCM, D’Aeth JC, Croucher NJ, (2019) doi: 10.1099/mgen.0.000305
The role of LSHTM and NU in this collaborative project
Prof. Yoshida is PI on the underlying PCV randomised trial on which this project is based. Prof. Toizumi, based in the School of Tropical Medicine and Global Health, is an expert on the clinical and data management aspects of the PCV trial and works closely with Prof. Yoshida on the clinical and field work aspets of the PCV trial. Profs. Toizumi and Yoshida will contribute important supervision related to epidemiological interpretation, clinical background and trial set up necessary to correctly analyse the data. Moreover, Prof. Yoshida is the co-investigator on the Wellcome-funded project that seeks to evaluate resistance endpoints from this trial and we have been working together since 2021 on this project. This Wellcome project uses mathematical modelling and bioinformatics but there is huge potential to extend this project by using phylogenetics, which is the topic of this proposed collaborative project. Prof. Yoshida and Dr. Atkins will continue to collaborate on this wider project, which will be complementary to this PhD project together with partners from the LSHTM and Sanger. Dr. Hué (second supervisor at LSHTM) is a long-standing collaborator of Dr. Atkins and Prof. Yoshida and have jointly supervised PhD students (including on this programme). The supervisor team comprises a complementary and interdisciplinary mixture of clinical, epidemiological, modelling and phylogenetic expertise.
Particular prior educational requirements for a student undertaking this project
Proven experience in one or more of the following is desirable: infectious disease epidemiology, phylogenetic analysis, mathematical modelling, one scientific programming language (e.g. R, C++, Python). The successful candidate will work in a highly interdisciplinary environment and should be able to work independently and as part of a distributed international team.
Skills we expect a student to develop/acquire whilst pursuing this project
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.