Combining infectious disease and economic modelling to inform decision‑making around antibiotic use targets and implementation of interventions

Antibiotic resistance occurs when antibiotics are no longer effective against the pathogenic bacteria they are designed to treat. It presents a serious public health challenge with economic and population health costs, and is largely driven by the unnecessary use of antibiotics. Improving identification or prevention of viral infections is one potential way to reduce unnecessary antibiotic use. Technologies such as vaccines and diagnostic tests already exist, but more research is needed to understand how they could be utilised most effectively and their impacts on antibiotic resistance.

The aim of my thesis project is to explore how the improved identification or reduction in incidence of viral infections may impact antibiotic resistance, specifically through the use of viral vaccinations and rapid point‑of‑care diagnostic tests.

The project will apply methodologies from economics, infectious disease modelling and statistics, and is comprised of a series of studies: (1) a systematic review of existing mathematical models in this area; (2) an assessment of health and AMR‑associated impacts of RSV vaccination and C‑reactive protein (CRP) diagnostic tests in the UK; and (3) the development of a transmission dynamic model to assess the potential health and economic impacts of RSV vaccination on infectious disease burden and on antibiotic resistance epidemiology in the UK.

Research on this project is ongoing. Outputs from this project are expected to help inform understanding of how viral dynamics impact antibiotic resistance epidemiology, potentially informing future intervention study design and policy.