Daniel Wilson
MA, DPhil
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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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
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.
Pascoe B. et al, (2024), The Journal of infection, 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
Ovi F. et al, (2024), Journal of Applied Microbiology, 135
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Identifying direct risk factors in UK Biobank with simultaneous Bayesian-frequentist model-averaged hypothesis testing using Doublethink
Arning N. et al, (2024)
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Quantitative measurement of antibiotic resistance in Mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach.
CRyPTIC Consortium None., (2024), Nature communications, 15