Imperial College London


Faculty of Natural SciencesDepartment of Mathematics

Reader in Statistics and Machine Learning for Public Good



oliver.ratmann05 Website




525Huxley BuildingSouth Kensington Campus





I develop bespoke statistical methods for public good.

I am particularly interested in novel Bayesian methods that harness information in viral deep sequence data, mobile phone mobility data, and time-resolved patient data to characterise the spread of infectious diseases, and to guide public health interventions. 

My group and I are involved in a number of multinational public health projects. See my research page for details:

Phylogenetics and Networks of Generalised HIV epidemics in Africa

Global reference group on children affected by COVID-19

Imperial College London COVID-19 Response Team

Long-term impact of universal treatment and dolutegravir on population HIV virologic and incidence outcomes in Africa

HIV transmission elimination initiative Amsterdam


I enjoy working with a really brilliant team, and within our Machine Learning & Global Health Network:


  • Dr. Alex Blenkinsop (post-doc, working on estimating children who lost their parent or caregiver to COVID-19, branching processes, and HIV phylogenetics).
  • Melodie Monod (PhD student, working on full Bayesian inference of age-specific transmission models with Stan, scalable inference with Gaussian process approximations, and reconstruction of transmission networks with Cox hazard models).
  • Andrea Brizzi (PhD student, working on age-specific fatality rate models, and semi-parametric models of group-level trajectories).
  • Yu Chen (PhD student, working on full Bayesian inference of transmission models with Stan, and high-resolution inference of human contact patterns).
  • Devrat Kaushal (PhD student, working on HIV phylogenetics).
  • Students on the MSc in Statistics, the MSci in Mathematics, our Mary Lister McCannon summer research fellows, and undergraduate research fellows who contributed substantially to research, especially Megan Andrews, Sofocleus Lysandros, Yining Chen, Zhaorui Zhang, Martin McManus, Vidoushee Jogarah, Imogen Kyle, Yuanrong Wang, Yixin Wang, Cathal Mills, Niamh Arthur, and Shozen Dan.

We work closely within our Machine Learning & Global Health Network, with Christophe Fraser's group at the Big Data Institute at Oxford, Samir Bhatt's group at the University of Copenhagen, Seth Flaxman's group at the University of Oxford, Kate Grabowski's group at John's Hopkins University and Susan Hillis' team at the CDC. We are always looking for creative and brilliant people to join our team, especially if you have an excellent background in Bayesian statistics, Stan, Gaussian processes, low rank GP approximations, and viral deep sequence analysis. Please just reach out by email and describe the type of our research you find most exciting, and how you would fit.


Some of our press coverage can be found here


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 Imperial in 2012. He started his own research group in 2017 with a focus on harnessing novel data streams and providing freely accessible statistical tools for public good, and informing public health interventions. Methodologically, Oliver is interested in phylodynamics, Gaussian process approximations, Stan, mathematical models of infectious disease dynamics, and survey and sampling methods.

Selected Publications

Journal Articles

Monod M, Brizzi A, Galiwango RM, et al., 2024, Longitudinal population-level HIV epidemiologic and genomic surveillance highlights growing gender disparity of HIV transmission in Uganda, Nature Microbiology, Vol:9, ISSN:2058-5276, Pages:35-54

Hillis S, Unwin H, Chen Y, et al., 2021, Global minimum estimates of children affected by COVID-19-associated orphanhood and deaths of caregivers: a modelling study, The Lancet, Vol:398, ISSN:0140-6736, Pages:391-402

Faria NR, Mellan TA, Whittaker C, et al., 2021, Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil, Science, Vol:372, ISSN:0036-8075, Pages:815-821

Volz E, Mishra S, Chand M, et al., 2021, Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England, Nature, Vol:593, ISSN:0028-0836, Pages:266-269

Monod M, Blenkinsop A, Xi X, et al., 2020, Report 32: Targeting interventions to age groups that sustain COVID-19 transmission in the United States, Pages:1-32

Ratmann O, Kagaayi J, Hall M, et al., 2020, Quantifying HIV transmission flow between high-prevalence hotspots and surrounding communities: a population-based study in Rakai, Uganda, The Lancet Hiv, Vol:7, ISSN:2352-3018, Pages:e173-e183

Chatzilena A, van Leeuwen E, Ratmann O, et al., 2019, Contemporary statistical inference for infectious disease models using Stan, Epidemics, Vol:29, ISSN:1755-4365

Ratmann O, Grabowski MK, Hall M, 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

Lachlan RF, Ratmann O, Nowicki S, 2018, Cultural conformity generates extremely stable traditions in bird song, Nature Communications, Vol:9, ISSN:2041-1723

Wymant C, Hall M, Ratmann O, et al., 2018, PHYLOSCANNER: Inferring Transmission from Within- and Between-Host Pathogen Genetic Diversity., Mol Biol Evol, Vol:35, Pages:719-733

Ratmann O, van Sighem A, Bezemer D, et al., 2016, Sources of HIV infection among men having sex with men and implications for prevention, Science Translational Medicine, Vol:8, ISSN:1946-6242

More Publications