A national serosurvey in Georgia in 2015 identified that the chronic prevalence of Hepatitis C virus (HCV) in adults in the country was 5.4%, and chronic infection (anti-HBc and HBsAg positive) with Hepatitis B virus (HBV) was 2.9% (1,2) . Building on this, the country launched an elimination program for HCV focused on scaling up access to treatment alongside prevention interventions. A repeat serosurvey in 2021 found that the elimination program had achieved a 67% reduction in chronic HCV prevalence, but that HBV prevalence remained at 2.7% among adults (3,4). The 2021 study also tested children and found that access to infant vaccination since 2001 has effectively controlled HBV in children (4). The country is now prioritising scaling up access to HBV treatment to meet the joint viral hepatitis (B&C) elimination goals set by WHO by 2030.

Previous work at the University of Bristol to model the HCV epidemic in Georgia and the impact and cost-effectiveness of scaling up treatment contributed to decision-making within Georgia’s National HCV elimination program (5,6,7). Similarly, modelling HBV in Georgia will assist the government with decision making regarding prioritisation of resources to reach HBV elimination.

Aims and objectives
This project aims to develop and parameterise a model of Hepatitis B virus infection in Georgia, and use the model to estimate the most cost-effective approaches for achieving HBV elimination.

1. Develop and calibrate a model of HBV transmission to represent the HBV epidemic in Georgia, in the general population and key risk groups
2. Simulate alternative intervention scenarios (including case-finding strategies) and assess their impact on HBV incidence and mortality over time to determine which reach the WHO elimination targets
3. Estimate the costs and cost-effectiveness of different intervention scenarios that reach elimination, including distributional cost-effectiveness analysis to address issues of equitable distribution of resources

Methodology
This project will build on previous work in Georgia and previous models developed by our research group on HBV for other settings. The student will learn skills in mathematical modelling of infectious diseases (compartmental deterministic models based on ordinary differential equations), including how to parameterise and calibrate models to data using Bayesian methods to account for uncertainty in parameter values. Based on previous HBV modelling undertaken in the team, the student will develop a specific model of HBV transmission for Georgia, which will be designed to incorporate the main modes of HBV transmission, maximise the use of available data, and answer key policy-relevant questions related to HBV elimination. This will involve coding in R and/or Matlab, and use of a high performance computing cluster.

The student will also need to manage, process and analyse data from various sources to utilise into the model. This will include epidemiological data, treatment rates, health-related quality of life data, and cost data. The student will have the opportunity to collect cost data in Georgia to represent the costs of different interventions.

The modelling process, aims, and results will be discussed regularly with Georgian stakeholders, and the student will learn about interpreting and presenting modelling results to influence policy.