Research

My research sits at the interface between mathematical epidemiology and health economics, using mathematics, statistics, and computation to understand different aspects of public health. I have particular expertise in stochastic processes, models of respiratory infections, Bayesian inference, and machine learning. Key areas of my work to date are listed below:

Household-structured epidemic models. I am particularly interested in developing new simulation and inference techniques for high-dimensional risk-stratified household models.

Economic assessment of cancer screening programs, including risk-stratified breast screening and new cervical cancer screening technologies.

Economic assessment of personalised medicine programs.

Models of health behaviour including vaccine uptake and responses to public health campaigns.

Eco-epidemiological modelling of avian influenza in wild birds using Bayesian machine learning.

COVID-19 policy modelling, focusing on the use of age- and household-structured models to understand the impact of non-pharmaceutical interventions. We were particularly interested here in answering questions relating to exemptions to and relaxations of lockdown measures: how effective is a lockdown likely to be if we allow for support bubbles? How disruptive is a temporary relaxation of lockdown rules for an event such as Christmas likely to be?

Analysis of new and emerging infections such as Ebola and MPox, and particularly the role of individual-level variability in person-to-person spread.

The role of demography in endemic childhood infections such as measles and mumps.