Katrina Lythgoe

In our group, we are interested in the evolutionary epidemiology of viral infections, including HIV, Hepatitis C and Hepatitis B. We use a combination of approaches, including population genetics, deterministic and stochastic modelling, and the evolutionary analysis of viral sequence data. More specifically, we are interested in evolutionary and ecological processes operating at different ecological scales (e.g. within- and between-host), to assess the impact this integration of scales has on our understanding of the evolution and epidemiology of infectious disease. Our ultimate aim is to produce better predictive models of the consequences of interventions, including the spread of transmitted drug resistance, changing levels of viral virulence, and adaptation of viruses to host immunological backgrounds.

To give more of a flavour of what we do, current projects include:

Within-host viral population structure and viral evolution

Viral populations within individuals are typically assumed to be well mixed, but reality is much more complex. In our group, we are using (1) phylogenetic approaches, utilising within-host deep sequencing data, to infer within-host viral population structure, and (2) modelling approaches to predict how within-host population structure affects the evolutionary dynamis of pathogens.

Rates of evolution of chronic viruses across scales

Rates of HIV and HCV evolution are slower when measured at the population scale compared to the within-host scale. This mismatch is probably a consequence of the selection pressures acting on viruses during infection and at the point of transmission. Using modelling approaches, and analysing within- and between-host sequencing data, we are unravelling these selection pressures with the aim of gaining a better understanding of virus transmission.

Consequences of within-host dynamics on evolutionary epidemiology

Because of high rates of within-host evolution, the virus(es) an individual transmits is unlikely to be identical to those they were infected with. We are using mathematical models to explore the consequences of within-host evolution on the evolutionary epidemiology of chronic infections, such as rate of spread of transmitted drug resistant mutations.