Our research investigates how biological, environmental and social factors combine to shape disease dynamics across scales — from processes within individual hosts to transmission between individuals and populations. By linking theory, data and computation, we aim to provide insight into the mechanisms driving infection, immunity, and pathogen evolution, and to inform strategies for disease prevention and control.

Mathematical approaches

We use a range of modelling frameworks, including systems of ordinary and partial differential equations, agent-based and network-based models, stochastic simulations, and control-theoretic approaches. Our work incorporates behavioural, demographic and spatial heterogeneity to explore how social structure and human movement influence transmission. We also integrate within-host and between-host modelling to study multi-scale interactions and evolutionary feedbacks in infectious systems.

Applications

Our research covers a broad spectrum of pathogens and systems. We have modelled how human mobility patterns influence vector-borne diseases such as dengue, how host specialisation and ecological diversity shape Borreliatransmission in wildlife, and how parasite management in grazing livestock depends on heterogeneous parasite distributions.

We also study the evolution of antimicrobial resistance (AMR) and host–pathogen coevolution, and investigate how microbial community interactions influence infection outcomes.

Recent work includes modelling the within-host dynamics of tuberculosis (TB) to understand immune response and treatment efficacy, and contributing to real-time modelling of COVID-19 transmission and control, providing insights into epidemic trajectories and intervention effects. We further explore how behavioural responses and information spread affect epidemic progression and control effectiveness.