Infectious disease models often assume that host populations are homogeneous, yet individuals vary widely in how frequently they contact others and how long they live. Such heterogeneity can strongly influence how pathogens spread, how large outbreaks become, and whether infections persist over time. Although these effects are well recognised in theory, there is limited empirical understanding of how individual-level variation shapes disease dynamics in natural populations.

The objective of the PhD is to quantify how individual differences in contact behaviour and lifespan influence viral transmission dynamics and long-term persistence in wild rodent populations. The student will analyse long-term capture-mark-recapture and social contact data from multiple rodent species, alongside viral genomic data collected over several years in a seasonally variable environment. These empirical data will be integrated with network and stochastic modelling as well as Bayesian inference approaches to explore how individual heterogeneity shapes epidemic size, timing, and persistence in populations living in dynamic habitats.

By combining detailed field-derived data with theoretical models, the project will advance understanding of disease dynamics in wildlife and generate insights relevant to the surveillance and control of rodent-borne viruses of public health concern.