Developing computational models to understand the effects of within-host viral dynamics on population-scale transmission and outcomes

Infectious diseases are responsible for substantial morbidity and mortality around the world. However, despite the global nature of the challenge of limiting the negative impacts of disease, population-scale transmission is influenced by pathogen dynamics arising at the individual-host level. The potential for an infected individual to infect others is linked to their pathogen load, as well as other factors such as their behaviour. Severe infections can have immediate negative consequences, such as host death, and there is accumulating evidence that even infections that do not immediately have adverse outcomes can lead to subsequent complications. It has been suggested that interventions such as vaccination may therefore reduce the risk of later complications, with recent evidence indicating that Shingrix vaccination (to prevent shingles) may be associated with a reduced risk of dementia several years later. This project focuses on viral infections. The core aim is to develop mathematical and computational models to explore the impacts of within-host viral dynamics on population-scale transmission and negative outcomes in host populations. Models will be built to characterise the within-host dynamics of a range of viruses, including Ebolavirus and Epstein-Barr virus. Key questions that the models will be used to answer include, among others: i) Which pharmaceutical and non-pharmaceutical interventions can be used to reduce the risk of large Ebola outbreaks?; ii) Which features of within-host dynamics could be responsible for the association between Epstein-Barr virus infections and subsequent onset of neurological disease? In addition to construction of the underlying models and modelling methods, the project will involve the development of software based on the models that can be used by other scientists and public health policy advisors. Potential uses include the design of public health measures during outbreaks and the guidance of vaccine clinical trials (by pharmaceutical industry colleagues; e.g. through facilitating selection of the study population). This project will require innovative approaches for linking within-host viral dynamics models and population-scale transmission models, and aligns with the EPSRC "Tackling infections" and "Healthcare technologies" research themes. It falls under the EPSRC mathematical biology research area. Key mathematical approaches that will be used in the project include stochastic and deterministic transmission modelling, Bayesian parameter inference and numerical solution of ODEs, among other techniques. A key challenge when considering the association between viral infections and neurological conditions is that acute viral infection and the onset of neurological disease typically occur years apart; novel techniques are required to bridge this temporal gap using models. This project therefore involves designing multi-scale epidemiological modelling frameworks that can be used to understand dynamics over long timescales. The project falls in line with the SABS CDT theme relating to modelling underpinning biomedical discovery. Through developing software and through collaboration with the industrial supervisors at GSK, this project is expected to have substantial positive real-world impacts.