Modelling Hepatitis C virus infection and treatment impact through serological surveillance data

Hepatitis C virus (HCV) remains a major public health concern in the United Kingdom (UK), particularly among people who inject drugs (PWID), who account for the majority of new and existing infections. Despite the availability of highly effective direct-acting antiviral (DAA) therapies and a national commitment to elimination, transmission continues in key populations. Monitoring progress toward elimination requires timely and accurate estimates of incidence of infection, but these are challenging to obtain using routine surveillance data. This thesis develops and applies statistical methods to estimate incidence, reinfection, and treatment uptake among PWID in the UK, using two key public health surveillance systems: the Unlinked Anonymous Monitoring (UAM) Survey and the Sentinel Surveillance of Blood- Borne Viruses (SSBBV). These datasets differ in design, with the UAM providing repeated cross-sectional snapshots with rich behavioural data, while the SSBBV offers longitudinal test histories, but without detailed behavioural covariates. Both present unique opportunities and challenges for modelling HCV infection and treatment patterns in marginalised populations. I first apply catalytic and multi-state models to UAM data to estimate injecting dura- tion–specific and time-varying HCV incidence. By incorporating serological and virological markers, these models are extended to account for reinfection and treatment-induced clearance. Simulation studies demonstrate that multi-state approaches can recover dynamic parameters under realistic assumptions, even when information on infection status and time at risk is available only at a single time point. These methods are then applied to real UAM data from 2011–2022 to provide post-DAA estimates of infection and clearance rates in this cohort. Next, a PWID sub-cohort is constructed from the SSBBV system and analysed using multi-state models to characterise longitudinal disease trajectories. The model is progressively refined to incorporate calendar time and risk group derived from testing behaviour, enabling temporal comparison with UAM-derived estimates. While the datasets differ in structure, their complementary strengths provide a robust triangulation of the processes of HCV infection, viral clearance and reinfection among PWID. This thesis demonstrates how routine surveillance data can be re-purposed using statistical models to estimate key epidemiological quantities, and highlights the value of both cross- sectional and longitudinal data for infectious disease monitoring. The methods developed here contribute to efforts to quantify HCV burden, evaluate elimination progress, and inform future public health strategy.

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2025

Author dc:creator

• Egan, Conor

Advisors dc:contributor.advisor

• De Angelis, Daniela
• Harris, Ross

• Injecting drug user
• Cross-sectional serosurveys
• Semi-parametric models
• Force of infection
• Multi-state models
• Hepatitis C

Licence

• https://www.repository.cam.ac.uk/bitstreams/bfe73ebe-b930-4bb7-807f-492041399dee/download
• http://purl.org/NET/rdflicense/allrightsreserved

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.124671

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/394974