2024-25 Project (Atkins & Hue)

Understanding the impact of vaccination on the spread of antibiotic resistance using phylogenetics



Dr Katherine Atkins at LSHTM
Email: katherine.atkins@lshtm.ac.uk


Dr Stéphane Hué at LSHTM
Email: stephane.hue@lshtm.ac.uk


 Project Summary

Vaccines against bacterial pathogens have been proposed as a means to combat antibiotic resistant infections (e.g. Lipsitch and Siber, 2016). 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.    

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 
Tonkin-Hill, G., Ling, C., et al. (2022), (2019) doi:10.1038/s41564-022-01238-1

Project Key Words

phylogenetic analysis; infectious disease; epidemiology; vaccine

MRC LID Themes

  • Global Health = Yes
  • Health Data Science = No
  • Infectious Disease = Yes
  • Translational and Implementation Research = No


MRC Core Skills

  • Quantitative skills = Yes
  • Interdisciplinary skills = Yes
  • Whole organism physiology = No

Skills we expect a student to develop/acquire whilst pursuing this project

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.     The student will learn to communicate their research through publication in peer-reviewed journals and presentation in scientific conferences. By working closely with experts in public health, sequence data, phylogenetic analysis and mathematical modelling, the student will become comfortable working within an interdisciplinary environment and interacting with a diverse scientific team.


Which route/s is this project available for?

  • 1+4 = Yes
  • +4 = Yes

Possible Master’s programme options identified by supervisory team for 1+4 applicants:

  • LSHTM – MSc Health Data Science

Full-time/Part-time Study

Is this project available for full-time study? Yes
Is this project available for part-time study? Yes


Particular prior educational requirements for a student undertaking this project

  • LSHTM’s standard institutional eligibility criteria for doctoral study.
  • Desirable: previous coding experience; phylogenetic experience

Other useful information

  • Potential CASE conversion? = No


 Scientific description of this research project

1. Project objectives   
a. Characterise the spatial epidemiology of Streptococcus pneumoniae drug resistance within the high burden settings of Nha Trang, Vietnam.  
b. Evaluate the local and regional spread of Streptococcus pneumoniae drug resistance using phylogenetics. 
c. Quantify the impact of pneumococcal conjugate vaccine on the spread of Streptococcus pneumoniae drug resistance.   

2. Techniques to be used   
The project will use an interdisciplinary combination of genetic sequence data analysis, epidemiology, and phylogenetic analysis.   

3. Confirmed availability of any required databases or specialist materials   
This project will use already-collected rich sequence data and complementary epidemiological data from a multi-year cluster randomized trial for a pneumococcal conjugate vaccine (PCV) in Vietnam.    

4. Potential risks to the project and plans for their mitigation.   
No foreseeable risks. The data are rich with numerous scientific questions that can be investigated.

Further reading

(Relevant preprints and/or open access articles)

Additional information from the supervisory team

  • The supervisory team has provided a recording for prospective applicants who are interested in their project. This recording should be watched before any discussions begin with the supervisory team.
    Atkins-Hue Recording