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.
Genomic epidemiology of Candida auris introduction and outbreaks in the United Kingdom
Kappel D. et al, (2024)
Risk of SARS-CoV-2 reinfection during multiple Omicron variant waves in the UK general population
Jia W. et al, (2024), Nature Communications
Target enrichment improves culture-independent detection ofNeisseria gonorrhoeaedirect from sample with Nanopore sequencing
Street TL. et al, (2024)
The burden of bacterial antimicrobial resistance in the WHO African region in 2019: a cross-country systematic analysis
ROBLES AGUILAR G., (2023), The Lancet Global Health
Deep Reinforcement Learning for Multi-class Imbalanced Training: Applications in Healthcare
YANG J. et al, (2023), Machine Learning