The Small Animal Veterinary Surveillance Network (SAVSNET) collects large volumes of anonymised health records from a sentinel network of UK veterinary practitioners in real-time (savsnet.co.uk). Recently, we have used these data to describe antibiotic prescription in veterinary practice, to look at the seasonality of important parasites like ticks and flystrike. We also publish surveillance reports regularly in the Veterinary record. Data is fed back to participants in the form of anonymised benchmarks.
With Dogs Trust support, we are now advertising three PhDs to join an exciting interdisciplinary project based in leading research groups. The aim of this project (SAVSNET-Agile) is to speed up the flow of meaningful research surveillance measures so that they can be actioned in a clinically relevant time frame to improve individual and population canine health. These interlinked PhDs are i) in rapid statistical analysis of large data streams for anomalies (University of Lancaster), ii) in providing rapid decision support to clinicians to help better manage chronic disease (Liverpool – this PhD) and iii) developing a novel national framework to respond effectively to significant events like outbreaks in dogs (Bristol / Animal Health Trust). These PhDs will work closely together and with the rest of the SAVSNET team including software development and machine learning capability.

Clinical records represent a huge un-tapped resource for modelling clinical outcomes associated with patient characteristics and clinical history. In this specific PhD, we will identify groups of patients that are obese or have been diagnosed with diabetes mellitus and using these we will develop computer and statistical techniques to identify patient phenotypes associated with these conditions. The models produced will be tested for their ability to predict clinical outcomes with a view to incorporating them into real-time clinical support.

The successful candidate will be based at the University of Liverpool Leahurst Campus but will also spend time in Liverpool and possibly Manchester University. The candidate will learn a range of statistical (primarily using R) and software skills (primarily using the Python programming language) required to identify relevant clinical records and automate annotation of these records for the key clinical features that will allow modelling and prediction of clinical outcomes of diabetes and obesity.

This project will allow a clinically or para-clinically trained applicant to acquire the technical skills required to apply their clinical knowledge to this exciting challenge. Alternatively, a computer science or statistics graduate will be able to hone their data-mining/modelling skills in a health informatics setting.

This exciting PhD will complement the work of the other PhDs at Lancaster and Bristol, the wider SAVSNET team and the Dogs Trust, to transform the way big data is used in veterinary practice, surveillance and research when confronted with important canine disease.
During the PhD, there will be regular meetings to link the work of the three PhDs into the wider SAVSNET team, and to the Dogs Trust,
As well as traditional research publications, the results of this degree will feed directly back to practitioners through existing benchmarking sites. This will ensure the results of this work are rapidly translated into best veterinary practice.
The three-year project will be based at the University of Liverpool Leahurst campus, and is expected to start around September 2019. A student stipend of up to £20,000 is available for this project. All necessary postgraduate fees will also be covered at a UK / European rate. International (none-EU) students are welcome to apply but must be able to fund the difference between UK/EU and international tuition fees.

For enquiries please contact:
Dr P-J.M.Noble (rtnorle@liv.ac.uk)
Professor A.J.German (ajgerman@liv.ac.uk)

When applying via: http://www.liverpool.ac.uk/savsnet. Please give the names and addresses of two people who have agreed to act as referees on your behalf and provide their references. Referees should not be related to you.