Next Generation Farrington Model for Infection Prevention/Control: A Farrington Algorithm with Spatiotemporal & Adaptive Baseline Capabilities

About the Project

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.

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 and publish in peer-reviewed journals. They will work within an active group of statisticians who specialise in applied methodology and healthcare-related problems.

The goal is to create tools that help IPC teams detect problems earlier and prevent outbreaks before they escalate.

Funding Notes

A highly competitive Studentship, supported by the Healthcare Infection Society (HIS) and based in the School of Mathematics, providing the award of full academic fees, together with a tax-free maintenance grant of £21,805 per year for 3.5 years. Training and support will also be provided. This opportunity is open to UK applicants only. All candidates will be placed into the HIS/Mathematics Studentship Competition and selection is based on academic merit.