Complex systems · Spreading processes · Epidemiology

Spreading processes in adaptive systems — where behaviour and disease shape each other

Mathematical modelling of epidemics, behaviours, and the temporal contact networks that carry them.

An epidemic front travelling across a spatial contact network: recovered nodes on the left, infected nodes with faded, adapted links in the middle, susceptible nodes on the right

My work

Research themes

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Behaviour-driven epidemics

Diseases spread on populations that react to them. People test, protect themselves, or change whom they meet as their perception of risk shifts, and the disease responds in turn. I study this feedback with models in which behaviour and infection dynamics co-evolve — from the interplay between risk perception and the spread of COVID-19 to the testing paradox of bacterial STIs among MSM on HIV PrEP. A central question is which interventions remain effective once the population adapts to them.

Controllability of outbreaks

How much control over an outbreak is possible, and at what cost? Using minimal models as instruments, I have worked on the limits of test-trace-and-isolate, the stable low-case-number regime and the tipping point beyond which spread self-accelerates, relaxing restrictions at the pace of vaccination, and the complex — even chaotic — dynamics that emerge in the endemic state when moderate mitigation interacts with seasonality.

Adaptive networks, and modelling beyond epidemics

Contacts are not static: they are temporal, structured and adaptive. I work on spreading processes on such networks — risk-mediated regulation of contacts in metapopulations, household structure as a driver of incidence, sampling strategies for genomic surveillance — and keep several active collaborations in other fields where a bit of extra modelling helps, from glucose–insulin dynamics and protein engineering to cancer genomics. If you feel like your research could use some extra modelling, let's get in touch.