About the Project
Viral outbreaks, for example due to spillovers from one species to another, the emergence and spread of new variants, or the introduction of known viruses into new settings, can be devasting. Whilst many outbreaks subside, others can become established in their host population, either rapidly like SARS-CoV-2, or more gradually, like HIV, with their effects lasting for decades or more.
To control them, and evaluate control strategies, we need to discover the core characteristics of the pathogen, and how it spreads, as soon as possible. Precision epidemiology is a powerful approach to gain information on these characteristics quickly and efficiently, with example datasets including dense sampling of outbreaks in closed settings or households, contact-tracing, and repeated sampling of the same individuals. However, current epidemiological methods come with large uncertainties. Viral sequences can help reduce this uncertainty, but often much of the genetic data available isn't used: an infection with a rapidly mutating virus quickly results in a diverse and evolving population, and although next generation sequencing (NGS) captures this diversity, in epidemiological studies only a single 'consensus' sequence is typically used.
Aims
During this project you will take a data-driven approach to unlock the huge amount of intrahost genetic data available in the era of mass pathogen sequencing. You will join the IntrEpid (Utilising INTRahost pathogen variation for precision EPIDemiology) team, led by Professor Katrina Lythgoe at the University of Oxford, located in the Department of Biology and a member of the Pandemic Sciences Institute. Work by the team has been published in Nature and Science among other high-impact journals.
The ultimate aim of IntrEpid is to harness within-host diversity and evolution, combined with advanced statistical approaches including phylogenetics, explainable AI and multiscale modelling, that will enable researchers and public health professionals to reliably extract data from NGS data and provide a biologically motivated toolkit for both established and emerging viruses. Directions you could take during your DPhil towards this aim include developing methods to incorporate within-host diversity in phylogenetic methods, incorporate within-host viral dynamics into transmission models and simulations, to design optimal surveillance strategies, and to design pipelines enabling within-host data to be analysed within the trusted research environments commonly used in public health.
Methods
The methods you use will depend on the direction you choose to take, but could include bioinformatics, phylogenetics, advanced statistics, machine learning and/or software engineering. The DPhil will be most suitable for candidates with an aptitude for quantitative or computational methods and a knowledge or demonstrated interest in bioinformatics and/or evolution, ideally of viruses. Training will be provided, as will be the opportunity to attend residential specialist training courses.
Project code
KL01
Funding
This project is part of the DPhil in Biology programme, and is not a funded course at the University of Oxford; as such, students are expected to explore options for funding. However, we anticipate being able to offer around 6 full graduate scholarships to incoming DPhil students in 2027/28.
You will be automatically considered for several Oxford scholarships, which cover fees and stipend, if you fulfil the eligibility criteria and submit your graduate application by 6 January 2026. Scholarships are awarded based on academic achievement and potential to excel as a DPhil student.
For further details about searching for funding as a graduate student visit the graduate study information on our website.
Eligibility
For full entry requirements and eligibility information, please see the main admissions page.
How to apply
The deadline for applications for 2027/28 entry is 6 January 2027. We will continue to accept applications submitted after this date, but these late applications will not be considered for scholarship funding.
You can find the admissions portal and further information about eligibility and the DPhil in Biology Programme at the University's graduate admissions page. Please quote the Project Reference Code alongside the title in the 'Proposed field and title of research project, if applicable' field in the application form.
Institution website: https://www.biology.ox.ac.uk/dphil
References
Lythgoe et al (2021) SARS-CoV-2 within-host diversity and transmission. Science 372(6539), eabg0821
Ghafari et al (2024) Prevalence of persistent SARS-CoV-2 in a large community surveillance study. Nature 626 (8001), 1094-1101
Markov et al (2013) The evolution of SARS-CoV-2. Nature Reviews Microbiology 21 (6), 361-379