Professor Daniel Wilson
Research groups
- Analysing large-scale electronic health records to identify at-risk populations
- Identifying novel antimicrobial resistance genes from public databases
- Infectious Disease Epidemiology Unit
- Pathogen population genomics methods and applications
- Statistical methods for joint human-pathogen genome-wide association studies
Daniel Wilson
Professor of Infectious Disease Genomics
I am Professor of Infectious Disease Genomics at the University of Oxford. I hold a Robertson Fellowship at the Big Data Institute, part of Oxford Population Health, and serve as Director of Studies in Data Science at the Department for Continuing Education. My research is supported by the Robertson Foundation. I am a collaborator with the Modernising Medical Microbiology consortium based at the John Radcliffe Hospital, Oxford, and Associate Member of the Department of Statistics.
My research interests are in data science, infectious disease and pathogen genomics. I develop new tools for data analysis, and help facilitate infection research through collaborative projects. My research activity has focused on the analysis of bacterial diseases and populations. Motivating themes in my work are understanding genetic and non-genetic risk factors for infection, and the effects of transmission, natural selection and recombination in shaping pathogen diversity. I have investigated the evolution of pathogen populations from the colonisation of individual hosts and transmission between hosts, through to the whole-species and phylogenetics levels.
Recent publications
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Assessing the value of linking public health microbiology data to the UK Biobank
Preprint
Lin S-K. et al, (2025)
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Multiple introductions of NRCS-A Staphylococcus capitis to the neonatal intensive care unit drive neonatal bloodstream infections: a case-control and environmental genomic survey.
Journal article
Lees EA. et al, (2025), Microb Genom, 11
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Independent repeated mutations within the alphaviruses Ross River virus and Barmah Forest virus indicates convergent evolution and past positive selection in ancestral populations despite ongoing purifying selection
Journal article
Pyke AT. et al, (2024), Virus Evolution
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Machine learning to attribute the source of Campylobacter infections in the United States: A retrospective analysis of national surveillance data.
Journal article
Pascoe B. et al, (2024), J Infect, 89
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Evaluating the effects of virulence genotype, swarming motility, and multi-locus sequence types of Escherichia coli on layer chicken embryos
Journal article
Ovi F. et al, (2024), Journal of Applied Microbiology, 135