Respiratory viruses, such as RSV, influenza and Covid-19,
exhibit disparities in impact by ethnicity in England [ref 1].
The reasons for these differences are not fully elucidated
and evidence is needed to understand drivers of
disparities and to design strategies that could mitigate
them [ref 2].

Social contact data are frequently used to parameterise
transmission models of respiratory viruses, but until
recently, social contact data stratified by ethnicity or
socioeconomic status have not been available. This project
will analyse new data from the UK Social Contact Survey
survey, in which participants have reported their own and
their contact’s ethnicity. This will allow the student to
estimate mixing matrices stratified by age and ethnicity
and characterise resulting assortativity.

The student will use these data to develop a transmission
model stratified by age, ethnicity and (if feasible)
socioeconomic status (similar to models stratified by
deprivation [ref 3]) for one of the named viruses and fit
this model to available data using Bayesian methods. The
model can be used to design and evaluate mitigation
strategies if those are indicated, such as via vaccination
(ref 4), and determine if those affect health inequalities.
Examples could include: a) modelling and analysis of
seasonal influenza vaccination in older adults and
children, b) pandemic modelling and preparedness, c)
analysis of Covid vaccination (retrospective or
prospective), d) evaluation of health inequalities of the RSV
vaccination programme.

There is scope for part of the project to focus on specific
interests of the candidate in this area, for example
analysis of health datasets, methodological development
(e.g. agent-based models), or health economics.
The principal supervisors will be Professor Rosalind Eggo
(LSHTM) and Professor Neil Ferguson (Imperial). Professor
Eggo is a leading researcher in modelling of directlytransmitted pathogens, with a major research focus on
health inequalities of infections. Professor Ferguson is an expert in infectious disease modelling, parameter
inference and policy-relevant research. The student will
also benefit from supervision from a leading researcher at
UKHSA, who we will identify based on the focus of the
project.

1. Mathur et al 2020
   https://www.thelancet.com/article/S0140-67362100634-
   6/fulltext
2. Wing et al 2022
   https://academic.oup.com/ije/article/51/6/1745/6665821
3. Goodfellow et al 2024
   https://link.springer.com/article/10.1186/s12916-024-
   03387-y
4. Baguelin et al 2013
   https://journals.plos.org/plosmedicine/article?id=10.1371/j
   ournal.pmed.1001527