Dr Katrina Lythgoe
Colleges
Katrina Lythgoe
Group Leader
- Sir Henry Dale Fellow
- Research Group Leader
- Tutorial Fellow, Brasenose College
I am a Group Leader at the Big Data Institute, Associate Professor in the Department of Biology, and a Tutorial Fellow at Brasenose College.
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.
More specifically, we are interested in evolutionary and ecological processes operating at different ecological scales, such as within and between individuals, to assess the impact this integration of scales has on our understanding of the evolution and epidemiology of infectious diseases. Recently, we have also started to think about designing tools and strategies enabling us to optimise the genomic surveillance of viruses in different settings, and at different stages of an outbreak.
We are a diverse and interactive group consisting of postdocs, graduate students, 4th year MBiol students, and undergraduates. Some of our current projects 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
Recent publications
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Viral burdens are associated with age and viral variant in a population-representative study of SARS-CoV-2 that accounts for time-since-infection related sampling bias
Preprint
Fryer HR. et al, (2022)
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Genomic epidemiology of SARS-CoV-2 in a university outbreak setting and implications for public health planning
Journal article
Nickbakhsh S. et al, (2022), Scientific Reports, 12
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The mutational spectrum of SARS-CoV-2 genomic and antigenomic RNA
Journal article
Zhao L. et al, (2022), Proceedings of the Royal Society B: Biological Sciences, 289
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Using phylogenetics to infer HIV-1 transmission direction between known transmission pairs
Journal article
Villabona-Arenas CJ. et al, (2022), Proceedings of the National Academy of Sciences, 119
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Analysis and comprehensive lineage identification for SARS-CoV-2 genomes through scalable learning methods
Preprint
Cahuantzi R. et al, (2022)