Dr Euan Bennet
- Research Associate (Equine Clinical Sciences )
I am a Postdoctoral Research Associate in the Division of Equine Clinical Sciences at the School of Veterinary Medicine. I started my career as a theoretical astrophysicist, qualifying from the University of Glasgow with an MSci in Astronomy and Physics (2008) before graduating with a PhD on plasma physics in the early Universe (2012). Following that I worked as a postdoc in the Astronomy and Astrophysics research group in the School of Physics and Astronomy (2013-2015), before being recruited for my current role as the number one [only] horse physicist in the world.
Since November 2015 I have been performing in-depth epidemiological analysis of the Global Equine Injuries Study for the Fédération Equestre Internationale (FEI) – the world governing body for equestrian sports. Initially my focus was on the sport of Endurance (long-distance riding over distances of 80km-160km) with data from every international-level event worldwide since 2010 – covering over 90,000 horse starts. In November 2017 the project was extended for a further two years, and expanded to include Eventing, which as an Olympic sport is more well-known than Endurance.
My work has provided scientific evidence of risk factors that lead to lameness or metabolic problems for horses riding in Endurance events. This has informed policy and protocol for future FEI Endurance events, and has now even changed the rules of this international sport. Major findings have included evidence of the negative impact of high riding speeds and short rest periods between events. In November 2017 my results were a crucial part of the evidence behind changes to Endurance rules which will come into effect in January 2019 (see also: http://inside.fei.org/news/fei-extends-global-equine-injuries-research-agreement-glasgow-university-further-two-years).
Statistical/Epidemiological analysis including:
- Multivariable logistic regression - to understand the impact for horses exposed to many risk factors at once
- Data mining - to understand the root causes of risk
- Predictive modelling – given the data from past events, it’s possible to predict which horses are amongst those exposed to the greatest risk before their next event.
Current projects include:
- Endurance: developing a predictive model to use “live” during events
- Eventing: building horse-, rider-, and event-level models to understand risk factors leading to horse and rider fatalities
- General: analysing the impact of weather conditions (temperature and humidity) as a significant contributing factor to metabolic problems.
I teach part of the module “Scientific Methods and Statistics 4” within the BSc Honours Veterinary Bioscience degree.