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

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.<br /> <br /> 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.<br /> <br /> 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.<br /> <br /> 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.<br /> <br /> 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.<br /> <br /> 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.<br /> <br /> 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.

<p>This is a fully funded 3.5-year PhD project supported by the Healthcare Infection Society (HIS). It is an established and approved project, and funding is available for UK/Home students only.</p> <p>This PhD focuses on developing new statistical tools for detecting unusual patterns in healthcare-associated infections. Infection Prevention and Control teams track infection numbers every day so they can act quickly when something looks unusual. However, most of the methods they use were created for simpler healthcare systems. They do not always cope well with changes in diagnostics, differences between hospital sites or shifts in how surveillance is carried out over time. A well-known example is the Farrington Flexible model. It is widely used and performs well in many settings, but it struggles when the underlying system changes or when infection patterns vary across locations. This project aims to build a model that is more flexible, more realistic and better suited to today’s surveillance needs.</p> <p>The work will build on the classical framework used in the Farrington approach. The aim is to create a version that brings in spatial information and more adaptive baselines. Instead of looking only at time trends, the new model will also capture how nearby hospitals or Trusts may influence each other, and how infection risks behave across different locations. Healthcare-associated infections do not occur in isolation, and the local environment, population and hospital characteristics all matter. The project will also handle changes in testing or recording practices, which often look like outbreaks but are not. By adding adaptive components, the model should remain stable and reliable even when surveillance practices evolve.</p> <p>During the PhD, the student will work with quasi-Poisson and related over-dispersed count models. They will apply these models to real surveillance data from Northern Ireland, using simulation studies and historical outbreaks to test how well the new methods work. This will give the student experience in both statistical model development and practical public-health application.</p> <p>A major strength of the project is the close relationship 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. These visits will show how surveillance challenges arise and how statistical methods can support real-world decision-making. The feedback from practitioners will help ensure the new model is practical, interpretable and genuinely useful for everyday IPC work.</p> <p>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.</p> <p>We welcome applicants 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 a motivation to work with real health systems will also be valuable.</p> <p>This project is suited to someone who enjoys developing new statistical methods and wants to make a direct contribution to public health. The goal is to create tools that help IPC teams detect problems earlier and prevent outbreaks before they escalate.</p>

<p>To apply for this project you will need to make a formal application for research degree study through the <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University website</a>. You will need to create a login ID with a username and PIN. </p> <p>•    For <strong>Application type</strong> please select <strong>Research Degrees – Research Postgraduate</strong>. <br /> •    The admission year for this project is <strong>2026/27</strong> Academic Year. <br /> •    You will need to select your <strong>Planned Course of Study</strong> from a drop-down menu. For this project, scroll down and select <strong>PHD Statistics FT</strong><strong>.</strong> <br /> •    The project start date for this project is <strong>1 September 2026</strong>, please use this as your <strong>Proposed Start Date of Research</strong>. <br /> •    Please state clearly in the research information section that the research degree you wish to be considered for is <strong>Next-Generation Farrington Model for Infection Prevention and Control: An Enhanced Farrington Algorithm with Spatiotemporal and Adaptive Baseline Capa</strong> as well as <a href="https://eps.leeds.ac.uk/faculty-engineering-physical-sciences/staff/10654/dr-sofya-titarenko">Dr Sofya Titarenko</a> and <a href="https://eps.leeds.ac.uk/maths/staff/4053/dr-haiyan-liu">Dr Haiyan Liu</a> as your proposed supervisors.</p> <p><strong>Please state in the Finance section that the funding source you are applying for is HIS/Mathematics Studentship 2026/27.</strong></p> <p>More information on how to apply is available on our website <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">here</a>. You will be required to provide a personal statement which outlines your interest in the project you are applying for, why you have chosen it and how your skills map onto the requirements of the project.</p> <p>Applications will be reviewed and assessed after the closing date of Tuesday 30 June 2026. We welcome and strongly encourage any potential applicants to contact the supervisor(s) for an informal discussion, prior to applying, and recommend submitting your application early.</p> <p><strong>Please note that you must provide the following documents in support of your application by the closing date of Tuesday 30 June 2026:</strong></p> <ul> <li>Full Transcripts of all degree study or if in final year of study, full transcripts to date including grading scheme</li> <li>Personal Statement outlining your interest in the project</li> <li>CV</li> </ul> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements below.</p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<p>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.<br /> <br /> 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.<br /> <br /> Please note that there is only 1 funded place available to UK applicants only. Please note that whilst you may be successful in securing an academic offer for any project linked to this funding opportunity, this does not mean that you have been successful in securing an offer of funding. Funding is awarded on a competitive basis.</p> <p><strong>Important:</strong> Please refer to the <a href="https://www.ukcisa.org.uk/">UKCISA</a> website or our <a href="https://www.leeds.ac.uk/undergraduate-fees/doc/fee-assessment">fee assessment page</a> for information regarding Fee Status for Non-UK Nationals.</p> <p><strong>Eligibility Criteria</strong></p> <ul> <li>Applicants must be eligible to pay fees at the Home (UK) rate.</li> </ul> <p>If you are unsure whether you are eligible for UK fees/funding, please see our <a href="https://www.leeds.ac.uk/undergraduate-fees/doc/fee-assessment">fee assessment page</a>.</p> <p><strong>Other Conditions</strong></p> <ul> <li>Candidates who have previously been awarded a PhD or are currently registered on a PhD are excluded from applying. Those who were previously studying for a PhD but did not complete may be considered. </li> <li>Awards must be taken up by 1st September 2026.</li> <li>Applicants must live within a reasonable distance of the University of Leeds whilst in receipt of this scholarship.</li> </ul>

<p>For further information about your application, including how to apply, please contact PGR Admissions by emailing <a href="mailto:phd@engineeering.leeds.ac.uk">phd@engineeering.leeds.ac.uk</a></p> <p>For further information about this project, please contact Dr Sofya Titarenko by emailing <a href="mailto:S.Titarenko@leeds.ac.uk">S.Titarenko@leeds.ac.uk</a> and Dr Haiyan Liu: <a href="mailto:H.Liu1@leeds.ac.uk ">H.Liu1@leeds.ac.uk </a></p>