Oliver focuses on developing statistical methods to characterise the spread of infectious diseases, and to guide public health interventions. He has developed novel ways of harnessing viral sequence data in combination with time-resolved patient data to measure the population-level impact of interventions, most notably on mitigating the burden of HIV.
STATISTICS FOR TACKLING INFECTIOUS DISEASES GROUP
Novel molecular epidemiological methods for next generation sequencing data (part of Phylogenetics and Networks of Generalised HIV epidemics in Africa);
Harnessing time-resolved patient records to contextualise and understand spread of HIV at the city level in Amsterdam, especially among men & women with a migration background (with HIV transmission elimination initiative Amsterdam);
New tools to characterise spread of HIV in Seattle USA in near real time (part of NIH R01 with University of Washington); please see the Research page for details.
Creative & Brilliant people
We are always looking for creative and brilliant people to join our team, especially if you have an excellent background in large-scale Bayesian modelling and scalable statistical computing, with molecular epidemiology a plus but not needed. We get many generic emails - please keep a few tips in mind:
PhD applicants: Please have a look at our papers. If you are still interested, check eligibility, email me (good time is October/November) with your CV, the type of our research you find most exciting, and how you would fit. Some funding options: Internal funding, Presidents, Maths Planet Earth, Ecology and Evolution, WT Bioinformatics, WT Infection Control, MRC DTP, plus a ROTH PhD studentship is available for excellent candidates to start any time.
Visiting students: We love to hear from you! Please have a look at our papers, and email me what area of our research you find most interesting, your CV, and arrange for a recommendation to be sent to me separately from one of your academic mentors.
Outstanding Post-Docs: There is flexibility in terms of the project you could get involve with. Please have a look at our research page and papers, and email me if you are still interested. Include your CV with your track record, how you would want to add to our research, and consider funding options: Chapman, 1851, Newton, Sir Henry Wellcome, MRC, NIHR.
VERY SHORT BIO
Oliver completed a Wellcome Trust funded PhD in Bayesian Statistics and Network Sciences in 2009 at Imperial College London, spent time overseas at Duke University in the U.S. as part of a Sir Henry Wellcome fellowship, and returned to the Department of Infectious Disease Epidemiology at Imperial in 2012. He joined Prof. Christophe Fraser's group in molecular epidemiology, with primary focus on developing methods for epidemiological analyses from NGS sequence data as part of the PANGEA-HIV consortium. Oliver started his group in 2017.
et al., 2019, Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis, Nature Communications, Vol:10, ISSN:2041-1723
et al., 2017, PHYLOSCANNER: Inferring Transmission from Within- and Between-Host Pathogen Genetic Diversity., Mol Biol Evol
et al., 2017, Phylogenetic Tools for Generalized HIV-1 Epidemics: Findings from the PANGEA-HIV Methods Comparison, Molecular Biology and Evolution, Vol:34, ISSN:0737-4038, Pages:185-203
et al., 2016, Sources of HIV infection among men having sex with men and implications for prevention, Science Translational Medicine, Vol:8, ISSN:1946-6234
et al., 2015, Dispersion of the HIV-1 Epidemic in Men Who Have Sex with Men in the Netherlands: A Combined Mathematical Model and Phylogenetic Analysis, Plos Medicine, Vol:12, ISSN:1549-1676
et al., 2015, PANGEA-HIV: phylogenetics for generalised epidemics in Africa, Lancet Infectious Diseases, Vol:15, ISSN:1473-3099, Pages:259-261
et al., 2012, Phylodynamic Inference and Model Assessment with Approximate Bayesian Computation: Influenza as a Case Study, Plos Computational Biology, Vol:8, ISSN:1553-7358
Rasmussen DA, Ratmann O, Koelle K, 2011, Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series, PLOS Computational Biology, Vol:7, ISSN:1553-734X
et al., 2009, Model criticism based on likelihood-free inference, with an application to protein network evolution, Proceedings of the National Academy of Sciences of the United States of America, Vol:106, ISSN:0027-8424, Pages:10576-10581