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CS - Dr Nicola Perra

Modelling the feedback between human behaviour and epidemic spreading 

Supervisor: Dr Nicola Perra

Project description:

Modelling the feedback linking human behaviour and the transmission of infectious diseases is one of the main challenges in Computational and Mathematical Epidemiology. 

Key obstacles are: 

1) the formulation of mechanisms able to synthesise such intertwined dynamics at different spatio-temporal scales 

2) the identification of effective empirical behavioural indicators (i.e., data proxies) 

3) the ability to capture heterogeneities across population strata. 

The project aims to tackle these issues by developing and fusing agent-based and compartmental models with the most recent advancements in Generative AI and Deep Learning. The objective is to create hybrid frameworks that can explore, map, and bridge the gap between macro-level, aggregate, dynamics and micro-level, individual, decision-making processes. By fusing these modelling paradigms, the project aims to develop more comprehensive and nuanced modelling capabilities by isolating key mechanisms, dynamics, and relevant indicators across spatio-temporal scales and population strata.  

The project will leverage the wide range of datasets collected and made available during the COVID-19 pandemic by companies (i.e., Facebook, Google, SafeGraph), researchers, and governments describing epidemiological data (e.g., cases, deaths, hospitalizations), relevant human behaviours (e.g., mobility, contacts patterns, vaccination uptake), and governmental intervention policies to guide and validate the modelling efforts. 

Furthermore, it will create large-scale synthetic data using agent-based models augmented, or not, with LLMs. 

Research in this area is critical for preparedness and response, as it might enhance our ability to anticipate, understand, and manage the complex socio-biological dynamics of future health emergencies. 

References:

  • Gozzi, N., Perra, N. and Vespignani, A., 2025. Comparative evaluation of behavioral epidemic models using COVID-19 data. Proceedings of the National Academy of Sciences, 122(24), p.e2421993122. 
  • De Gaetano, A., Bajardi, P., Gozzi, N., Perra, N., Perrotta, D. and Paolotti, D., 2023. Behavioral changes associated with COVID-19 vaccination: cross-national online survey. Journal of Medical Internet Research, 25, p.e47563. 
  • Li, Y., Gozzi, N. and Perra, N., 2025. Estimating behavioural relaxation induced by COVID-19 vaccines in the first months of their rollout. PLOS Computational Biology, 21(7), p.e1013266. 

Funding Notes:

This project is open to candidates applying for CSC Studentships and self-funded candidates.

Further information: 
How to apply 
Entry requirements 
Fees and funding

PhD Information Session 2026:
On Wednesday 14 January, we will be holding a short information session about PhD studies in Mathematics at QMUL. For full details about the event, please visit: https://www.qmul.ac.uk/maths/postgraduate/postgraduate-research/phd-information-session-2026/

 

As one of the UK’s most diverse universities, QMUL fosters an inclusive and supportive academic community.

The School of Mathematical Sciences is committed to the equality of opportunities and to advancing women’s careers. As holders of a Bronze Athena SWAN award, we offer family-friendly benefits and support part-time study.  

 

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