Led by Dr Ben Ashby, the goal of our research is to better understand the ecology and evolution of hosts and their parasites (“parasite” is taken in the broadest sense of the word to include bacteria and viruses as well as helminths and ticks).

We’re especially interested in coevolution, which is a process of reciprocal adaptations by hosts to defend themselves against parasites, and counter-adaptations by parasites to overcome or avoid host defences.

We use mathematical models and simulations to understand how parasites and the infectious diseases they cause evolve, for example to infect a broader or narrower range of hosts, or to become more or less virulent, and in turn, how hosts evolve to defend themselves through traits such as resistance, tolerance, and mate choice.

[Evolution, Medicine, and Public Health, 11 November 2022]

Objectives/aims: Prolonged infections of immunocompromised individuals have been proposed as a crucial source of new variants of SARS-CoV-2 during the COVID-19 pandemic. In principle, sustained within-host antigenic evolution in immunocompromised hosts could allow novel immune escape variants to emerge more rapidly, but little is known about how and when immunocompromised hosts play a critical role in pathogen evolution.

Materials and methods: Here, we use a simple mathematical model to understand the effects of immunocompromised hosts on the emergence of immune escape variants in the presence and absence of epistasis.

Conclusions: We show that when the pathogen does not have to cross a fitness valley for immune escape to occur (no epistasis), immunocompromised individuals have no qualitative effect on antigenic evolution (although they may accelerate immune escape if within-host evolutionary dynamics are faster in immunocompromised individuals). But if a fitness valley exists between immune escape variants at the between-host level (epistasis), then persistent infections of immunocompromised individuals allow mutations to accumulate, therefore, facilitating rather than simply speeding up antigenic evolution. Our results suggest that better genomic surveillance of infected immunocompromised individuals and better global health equality, including improving access to vaccines and treatments for individuals who are immunocompromised (especially in lower- and middle-income countries), may be crucial to preventing the emergence of future immune escape variants of SARS-CoV-2.

[Journal of Theoretical Biology, 7 April 2025]

Mathematical models of infectious disease transmission typically neglect within-host dynamics. Yet within-host dynamics – including pathogen replication, host immune responses, and interactions with microbiota – are crucial not only for determining the progression of disease at the individual level, but also for driving within-host evolution and onwards transmission, and therefore shape dynamics at the population level. Various approaches have been proposed to model both within- and between-host dynamics, but these typically require considerable simplifying assumptions to couple processes at contrasting scales (e.g., the within-host dynamics quickly reach a steady state) or are computationally intensive. Here we propose a novel, readily adaptable and broadly applicable method for modelling both within- and between-host processes which can fully couple dynamics across scales and is both realistic and computationally efficient. By individually tracking the deterministic within-host dynamics of infected individuals, and stochastically coupling these to continuous host state variables at the population-level, we take advantage of fast numerical methods at both scales while still capturing individual transient within-host dynamics and stochasticity in transmission between hosts. Our approach closely agrees with full stochastic individual-based simulations and is especially useful when the within-host dynamics do not rapidly reach a steady state or over longer timescales to track pathogen evolution. By applying our method to different pathogen growth scenarios we show how common simplifying assumptions fundamentally change epidemiological and evolutionary dynamics.