Using mathematical modelling to investigate the impact of interventions for preventing transmission of HIV and other blood-borne viruses among people who inject drugs

Abstract

People who inject drugs are at particular risk of blood-borne virus transmission due to the sharing of injecting equipment, leading to a huge burden of disease among this population. Mathematical modelling is vital for understanding the population-level impact of interventions to prevent the spread of infectious diseases, and is therefore widely used to inform policy decisions in this area. In this thesis, I aim to use mathematical modelling to support policy discussions regarding interventions for preventing blood-borne virus transmission among people who inject drugs.

A wealth of modelling evidence in this field already exists. However, there is currently no standard, systematic way of combining the results of existing modelling studies to gain a broad understanding of how the results apply across various settings. In this thesis, I explore an approach for retrospectively quantitatively synthesising the results of modelling studies. I implement this approach within a systematic review of modelling studies investigating interventions for preventing injecting transmission of hepatitis C. This review highlights the benefits of combination interventions and informs international guidance.

I also use modelling to support policy decision-making for a specific outbreak; the 2015 Glasgow HIV outbreak among people who inject drugs. To mitigate the spread of HIV, a novel community-based approach to treatment was implemented, alongside increased testing. My modelling suggests these interventions were key to bringing the outbreak under control.

Finally, modelling I conduct shows support for the use of treatment to mitigate HIV outbreaks among PWID. I show these results are robust to changes in model structure and assumptions, strengthening their use for policy decision-making. By considering the impact of modelling choices on results, and reflecting on the other work in this thesis, I suggest ways in which the general framework of modelling for policy decision-making could be improved.

Date of Award	20 Jan 2026
Original language	English
Awarding Institution	University of Bristol
Sponsors	GW4 BioMed MRC DTP
Supervisor	Ellen Brooks Pollock (Supervisor), Matt Hickman (Supervisor), Jane White (Supervisor) & Sharon Hutchinson (Supervisor)