Imperial College London


Faculty of Natural SciencesDepartment of Mathematics

Lecturer in Statistics



oliver.ratmann05 Website




525Huxley BuildingSouth Kensington Campus





The pace with which viral pathogens evolve means that closely related viruses contain information on who could have infected whom. This allows sophisticated techniques, typically Bayesian approaches, to reconstruct the spread of diseases, and design more precise interventions to stop it.

Major projects:

HIV Transmission elimination initiative Amsterdam

The HIV Transmission Elimination Initiative Amsterdam is an exciting new collaboration of all stakeholders in HIV prevention and care at the city level to develop and implement new strategies for reducing the number of new infections. We develop methods for addressing central questions such as how many individuals acquired HIV within the city; are hotspots of high prevalence also transmission hotspots; and what are the sources of new infections especially among men and women with a migration background? The data underpinning this study are perhaps unique in Europe and offer detailed insights into the individual-level context in which transmission occurred. Our analyses build on hierarchical Bayesian modelling of individual-level data, and viral phylogenetics. This work is funded through AIDSfonds.

Characterising the spread of HIV-1 in Seattle and King County, USA

Related to our work in Amsterdam, we are also involved characterising the spread of HIV in Seattle, USA. The particular focus is on better understanding the sources of new infections among adolescents and migrants, and how to stop them. Drawing parallels to the epidemic context in Amsterdam is a particularly exciting part of this work. This work is funded through the NIH.

Phylogenetics and networks of generalised epidemics in africa 

PANGEA-HIV is a very large consortium to inform HIV-1 prevention in sub-Saharan Africa from next-gen viral sequence data. More than 15,000 NGS have been generated to date, the largest such collection from sub-Saharan Africa. Working with NGS data offers unprecedented opportunities for characterising viral spread, but also in depth bioinformatics expertise. We contribute actively to phyloscanner, a software package in R and python for molecular epidemiological analysis of NGS data, and are now developing statistical source attribution methods for these new type of data. This work is funded by the Bill & Melinda Gates foundation.

Related to this work is the phylogenetics ancillary study of the HPTN071/ PopART HIV prevention trial in South Africa and Zambia.


Joshua Herbeck, Roxanne Kerani, University of Washington, Combined phylogenetic and epidemiological analysis to identify HIV infection sources in Seattle, WA, HIV intervention at the city level, 2016 - 2020

Godelieve de Bree, HIV Transmission Elimination Initiative Amsterdam, HIV prevention, 2015

Deenan Pillay, Christophe Fraser, Andrew Leigh-Brown, Tulio de Oliveira, Paul Kellam, PANGEA-HIV, Phylogenetics and Networks of Generalised HIV epidemics in Africa, HIV prevention in sub-Saharan Africa, 2013 - 2017