EPSRC Project Proposals 2026/27 - Next-Generation Farrington Model for Infection Prevention and Control: An Enhanced Farrington Algorithm with Spatiotemporal and Adaptive Baseline Capa

University of Leeds - Faculty of Engineering and Physical Sciences - Mathematics (Statistics)

Project link: Next-Generation Farrington Model for Infection Prevention and Control: An Enhanced Farrington Algorithm with Spatiotemporal and Adaptive Baseline Capa

Eligibility: UK only

Funding: A 3.5 year Studentship, supported by Healthcare Infection Society (HIS), providing the award of full academic fees, together with a tax-free maintenance grant at the standard UKRI rate of £21,805 per year for 3.5 years.

Lead Supervisor’s full name & email address
Dr Sofya Titarenko: S.Titarenko@leeds.ac.uk

Co-supervisor’s full name & email address
Dr Haiyan Liu: H.Liu1@leeds.ac.uk

Project summary

The project focuses on developing new statistical methods for detecting unusual patterns in healthcare-associated infections. This is a fully funded 3.5-year PhD project supported by the Healthcare Infection Society. It is an established and approved project. Funding is available for UK/Home students only.

Infection Prevention and Control teams monitor infection numbers every day, but the tools they currently use were designed for older and simpler systems. One well-known example is the Farrington Flexible model, which is widely used in surveillance. It works well in many situations, but it can struggle with modern challenges such as changes in diagnostics and differences between hospital sites. This PhD aims to create the next generation of outbreak-detection tools that can handle these issues more reliably.

The student will build on the classical Farrington model used in surveillance and develop methods that are more flexible and more realistic. They will apply the new models to real data from Northern Ireland and assess their performance using both simulations and historical outbreak information. The work combines methodological development with practical application in a real surveillance environment.

A key part of the project is the close collaboration with the Public Health Agency in Northern Ireland. The student will meet IPC nurses, epidemiologists and microbiologists, and will learn how outbreak decisions are made in practice. This will help ensure that the new methods are useful, robust and easy for IPC teams to use.

The student will be part of the research community in the School of Mathematics at Leeds and will take part in seminars, reading groups and opportunities to present their work at conferences.

We are looking for a student with a strong background in mathematics, statistics, medical statistics or a related quantitative area. Good programming skills (e.g. R, Python) are essential. An interest in statistical modelling of infectious disease data and in working with real-world healthcare systems will be helpful.

This project is well-suited to someone who enjoys developing new statistical ideas and wants their work to have a direct and positive impact on healthcare.

Please state your entry requirements plus any necessary or desired background

A first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. Ideal candidate will have some prior knowledge in deep learning and computer graphics.

Subject area: Statistics, AI & Machine Learning, Public Health & Epidemiology