The research of the group focuses on the evolution of viral infections using a variety of approaches including population genetics, deterministic and stochastic modelling, and the evolutionary analysis of viral sequence data. One or our key aims is to produce better predictive models of how viral populations evolve in response to change, be that in a new individual after a transmission event, in a population after a zoonotic jump, or in response to interventions such as immunisation or treatment.
Projects currently underway in the group include:
- Using viral deep and long-read sequencing data to understand the evolution and within-host population structure of viral populations such as Hepatitis C virus
- Determining how within-host evolutionary processes affect the evolution of viruses at the population scale, with a current focus on HIV
- Developing new methods to identify persistent SARS-CoV-2 infections and within-household transmission chains using data from the ONS Covid Infection Survey
- Designing optimal strategies for the genomic surveillance of viral pathogens
Specific topics that could be explored during a DPhil include, but are not limited to:
- Using Machine Learning and AI to uncover factors leading to the evolution and transmission of SARS-CoV-2 variants within and between host (such as vaccination, variant, etc.)
- Uncovering the reasons behind within-host hepatitis C population structure, including immune escape
- Determining which HIV variants rebound and are transmitted after treatment failure
- Determining optimal disease genomic surveillence stategies in resource limited settings
- Determining the role of Hepatitis B virus cccDNA transcriptional activity in increasing the lifespan of episomal cccDNA
As part of your DPhil you will:
- Develop and analyse evolutionary models of viral infection
- Learn to work with and analyse deep-sequencing and other data
- Be given the opportunity to attend a specialised course on modelling of infectious disease and/or analysis of viral genomic data, and to audit the Health Data Science CDT lectures, particularly those on the dynamics and evolution of infectious disease.
- Experience working in a collaborative team environment and presenting data at internal lab meetings, journal clubs and seminars
- Contribute data towards publication in a peer reviewed journal
Qualifications and Experience
PhD, Tutorial Fellow Brasenose College
