Sexually transmitted infections are a substantial and growing public health problem. In the UK, there are marked disparities in STI incidence rates between different ethnicities, between different regions, and between different socioeconomic groups. STI incidence rates are in part determined by patterns of sexual behaviour, but previous research has suggested that some differences in STI incidence rates (e.g. between ethnicities) cannot be fully explained by individual-level differences in sexual behaviour. However, only limited research has been done to explain the underlying drivers of the observed heterogeneities in STI transmission when transmission is considered as a dynamic process (a single known example being a 2004 study, looking at gonorrhoea in 3 London health authorities).

Alongside individual sexual behaviour, disparities in STI incidence may occur because of assortativity between different population groups, because of network structure (e.g. differing levels of concurrent partnerships), because of different screening rates between population subgroups, or some combination of these different factors. Transmission-dynamic modelling is required to understand whether these differences can explain observed patterns, and this project will explore whether these differences can occur when STI transmission is simulated across a population with realistic sexual mixing patterns and testing rates.

This PhD project will aim to:

1. Design and calibrate subgroup-stratified models of STI transmission in the UK, to to understand the extent to which a) differences in sexual behaviour, b) differences in mixing between groups, c) different network features (concurrency), and d) differences in testing rates explain differences in STI incidence rates between population subgroups.
2. Using a suite of appropriately calibrated models, to understand the extent to which model stratification impacts the predicted effectiveness of different STI control measures.
3. To understand whether STI control measures reduce or exacerbate inequalities, examining a) measures that do not depend on network structure (e.g. vaccination) and b) measures that utilise network structure (e.g. partner notification).

Supervisors:
Lead supervisor: Dr Trystan Leng, Lancaster Medical School, Lancaster University
Co-supervisor: Dr Sam Moore, Lancaster Medical School, Lancaster University

This PhD opportunity is being offered as part of the LSTM and Lancaster University Doctoral Training Partnership. Find out more about the studentships (https://www.lstmed.ac.uk/mrc_dtp_case) and how to apply (https://www.lstmed.ac.uk/study/research-degrees/lstm-mrc-doctoral-training-partnership/mrc-dtp-guidance-notes)

For enquiries please contact Dr Trystan Leng: t.leng@lancaster.ac.uk