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.
Recent publications
Clinical validation of a novel metagenomic nanopore sequencing method for detecting viral respiratory pathogens: diagnostic accuracy study
Preprint
Sanderson ND. et al, (2026)
How ready are we to use artificial intelligence in our fight against Antimicrobial resistance? An ESGAID and EAAS perspective.
Journal article
Giacobbe DR. et al, (2026), Expert review of anti-infective therapy
The cumulative incidence and infection hospitalization risk of SARS-CoV-2 by variant: a longitudinal study in England.
Journal article
Gaughan C. et al, (2026), American journal of epidemiology, 195, 188 - 197
Treatment of enteric fever (typhoid and paratyphoid fever) with cephalosporins: a Cochrane review
Journal article
Kuehn R. et al, (2026), EMERGENCIAS, 38, 66 - 68
Benchmarking transformer-based models for medical record de-identification in a single center multi-specialty evaluation.
Journal article
Kuo R. et al, (2025), iScience, 28