Predicting the Epstein-Barr Virus vaccine induced response required to protect against Multiple Sclerosis

Epstein-Barr Virus (EBV) is a herpesvirus that infects a large proportion of the global population. EBV has recently been associated with the subsequent onset of Multiple Sclerosis (MS). This suggests that EBV vaccination may offer a preventive strategy against MS by reducing the prevalence of EBV.

The research in this project involves the development of mathematical models to understand the interplay between EBV vaccination, EBV and MS. The overall aim is to investigate how targeted EBV vaccination strategies may reduce the number of MS cases. This will be done through the development of both population-scale EBV transmission models and within-host models of EBV dynamics and immune interactions.

The models will be used to provide insights into the pathways through which EBV may lead to MS and to quantify the potential impacts of vaccination strategies. As well as generating insights into the effectiveness of EBV vaccination at reducing numbers of MS cases, this research will provide an epidemiological modelling framework for studying the link between viral infections and neurological disease more generally.

In this project, we will develop and apply mathematical and statistical models to explore the relationships between EBV infection, immune response and MS risk, as well as to assess the potential impact of EBV vaccination on reducing MS incidence. At first, we will focus on the development of a population-scale model of EBV transmission dynamics and vaccination, with the following initial objectives:1.

Investigate data availability: We will identify patient data (both EBV-infected and uninfected individuals) and previous analyses that can inform the models that we develop. If sufficient data are publicly available, we will undertake our own statistical analyses to explore how population heterogeneities (e.g., host age, immune status) influence the link between EBV and MS, allowing for an improved understanding of the populations that might be expected to benefit most from targeted vaccination strategies.

2. Develop mathematical models: We will develop population-scale mathematical models of EBV transmission dynamics that can be used to project numbers of EBV-linked MS cases, accounting for heterogeneities in host populations. To our knowledge, no dynamical models currently exist linking numbers of EBV infections directly to MS prevalence. However, our models will build on a previously published population-scale model of EBV dynamics in England [1].

3. Analyse vaccination scenarios: Using the model that we develop, we will project the effects of different EBV vaccination scenarios on MS risk, including strategies that target those individuals who are most at risk. This will also involve assessing the impacts of different vaccination parameters, such as the duration of protection and effectiveness (considering the full range of model parameters that may be influenced by vaccination), to inform optimal deployment strategies for EBV.

As described above, this project will involve the development of novel models linking the prevalence of EBV to the prevalence of MS. Mathematical approaches that will be used over the course of the project include deterministic and stochastic transmission models, numerical solution of ODEs and Bayesian parameter inference. In the later stages of the DPhil, we hope to link within-host models of EBV dynamics and population-scale transmission models, requiring novel multi-scale epidemiological modelling frameworks.

This project falls within the EPSRC Mathematical Biology research area. It aligns with the "Tackling infections" and "Healthcare technologies" research themes.

The project will be carried out in collaboration with GlaxoSmithKline (GSK), with academic supervision from Prof. Robin Thompson and Prof. Philip Maini (Mathematical Institute, University of Oxford). Industrial supervision will be undertaken by Dr Anna Sher and Dr Rajat Desikan (Clinical Pharmacology Modelling and Simulat