Professor David Eyre
- Robertson Fellow
- Infectious Diseases Clinician
My research interests include the use of whole-genome sequencing as a tool for understanding the epidemiology and transmission of bacterial and fungal pathogens. My previous work has described the transmission of the major healthcare-associated pathogen Clostridium difficile and has also included large-scale sequencing projects tracking the spread of gonorrhoea and the emerging multi-drug resistant fungus Candida auris. I am currently working on developing mathematical models for pathogen transmission that allow risk factors for transmission to be identified, as a means to suggest potential interventions to prevent infections spreading.
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. I have developed methods using sequencing data to detect the presence of infection, e.g. from orthopedic devices removed from patients, as well as predict antibiotic resistance, e.g. in Enterobacteriaceae and Neisseria gonorrhoeae.
Additionally I work on using routinely collected healthcare data to investigate the epidemiology of infectious diseases and to investigate individual patient responses to infection and treatment.
I work closely with the Modernising Medical Microbiology consortium on several of these projects.
SARS-CoV-2 antibody responses post-vaccination in UK healthcare workers with pre-existing medical conditions: a cohort study.
Ward V. et al, (2022), BMJ open, 12
Treatment of enteric fever (typhoid and paratyphoid fever) with cephalosporins
Kuehn R. et al, (2022), Cochrane Database of Systematic Reviews, 2022
Benchmarking taxonomic classifiers with Illumina and Nanopore sequence data for clinical metagenomic diagnostic applications
Govender KN. and Eyre DW., (2022), Microbial Genomics, 8
RapiD_AI: A framework for Rapidly Deployable AI for novel disease & pandemic preparedness
Youssef A. et al, (2022)
Detecting changes in population trends in infection surveillance using community SARS-CoV-2 prevalence as an exemplar
Pritchard E. et al, (2022)