Estelle is a DPhil student in Health Data Science at the University of Oxford, supervised by Prof. Jasmina Panovska-Griffiths and Dr Jess Enright. Her research combines network theory, mathematical modelling, and digital epidemiology to investigate how patterns of social interaction shape the spread of infectious diseases.
Her work focuses on developing analytical tools to analyse dynamic social-contact networks, integrating real-world social behaviour into infectious-disease models to inform targeted interventions such as optimised testing, vaccination, and closure strategies. Using data-driven network models and Epidemica, a gamified mobile app that captures real-time close-proximity interactions, she investigates how social structure influences transmission dynamics and public-health outcomes. As part of the PRESTO project, she is also improving the categorisation of mpox breakthrough cases to support vaccine-trial optimisation and outbreak-response modelling.
Before beginning her DPhil, she completed her MMath at the University of Edinburgh, focusing on applied mathematics and statistics. Her master鈥檚 dissertation explored how science-informed synthetic data can be generated to model real-world processes more effectively. Alongside her studies, she worked part-time in agri-tech within the vertical farming sector. Her work involved building convolutional neural networks to automate plant monitoring, analysing data, and developing crop-testing methodologies.
Although she loved working in plant tech in the end she thought 鈥淧eople are more interesting than plants鈥 and was drawn to the health data field as a way to use her quantitative skills to improve public health. In the future she’d like to continue modelling disease dynamics whether that be in academia or industry.
