Professor David Eyre
Professor of Infectious Diseases
- Robertson Fellow
- Infectious Diseases Clinician
My research aims to understand who gets different infections and why, and how best to prevent, treat and monitor these infections. I also work on developing artificial intelligence tools to help diagnose and treat hospital patients, and to help hospitals run better.
I use a range of approaches spanning epidemiology, statistics, causal inference, and machine learning. I work with detailed deidentified healthcare record data at both regional and national scales. I also have extensive programming and database expertise.
My other research interests include the use of whole-genome sequencing as a tool for understanding the epidemiology and transmission of bacteria, viruses and fungi, and mathematical modelling of infectious disease transmission. I am also interested in using sequencing technologies as a novel tool for culture-independent microbiology diagnostics. These technologies offer the prospect of same-day diagnosis of infection, rather than having to wait several days for bacteria to grow in the lab as is common now.
I work closely with the Modernising Medical Microbiology consortium on several of these projects, contributing to the Oxford NIHR Biomedical Research Centre and an NIHR Health Protection Research Unit.
The epidemiology of multidrug-resistant organisms in persons diagnosed with cancer in Norway, 2008-2018: expanding surveillance using existing laboratory and register data.
Danielsen AS. et al, (2023), Eur J Clin Microbiol Infect Dis
Deep Reinforcement Learning for Multi-class Imbalanced Training: Applications in Healthcare
YANG J. et al, (2023), Machine Learning
Persistence of SARS-CoV-2 antibodies over 18 months following infection: UK Biobank COVID-19 Serology Study.
Bešević J. et al, (2023), J Epidemiol Community Health
A scalable federated learning solution for emergency care using low cost microcomputing: Privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals
Soltan A. et al, (2023), The Lancet. Digital Health
The burden and dynamics of hospital-acquired SARS-CoV-2 in England.
Cooper BS. et al, (2023), Nature