I am a researcher in outbreak response analytics, with a background in biostatistics, population genetics, and R programming. My research focusses on developing new methodologies and tools for understanding how infectious diseases spread, and how we can control them.
I wear several hats, including:
You can find my CV here, and my PhD thesis (in French) there.
Outbreak response analytics
I am interested in developing a holistic approach to outbreak data analysis, with a strong focus on emergency outbreak response context, in which analytics directly inform public health decision making. Beyond infectious disease modelling techniques used in academia, I focus on the development of operational analysis tools, including reproducible and auditable data cleaning, interactive data visualisation tools, and automated report generation systems. On a more theoretical side, I am also interested in the estimation of key delay distributions (e.g. incubation period, serial interval distribution), and in robust estimations of transmissibility and the use of branching processes for short term incidence forecasting.
I regularly deploy to outbreak responses in the field, or close to it. In 2019, I will have spent a total of 6 months in North Kivu, DRC, for the response to the Ebola outbreak. I set up the analytics pipelines used first in Béni, then in Goma for informing the leadership of the response on various aspects of the outbreak in real time. Since February 2020, I have been working full-time on COVID-19, setting up data pipelines for the CMMID group at LSHTM, and developing statistical approaches for informing the response in the UK alongside many other members of SPI-M.
Evidence synthesis approaches for epidemics analysis
Part of my research focusses on integrating epidemiological and genomic data for analysing epidemics. I have pioneered the field of statistical outbreak reconstruction by publishing outbreaker in 2014, the first tool integrating epidemiological and genomic data for inferring who infects whom during an epidemic. I am supervising a PhD student on this topic, who carries further the integration of multiple data sources for outbreak reconstruction through the development of outbreaker2. I am also developing fast, scalable algorithms for outbreak detection by combining various type of data including spatial, temporal, and genetic information on reported cases.
RECON: the R Epidemics Consortium
In September 2016, I have create the R Epidemics Consortium (https://www.repidemicsconsortium.org), an international network of experts in infectious disease modelling, public health, and software developers interested in creating the next generation of tools for disease outbreak analysis using the R software.
In December 2017, I have created RECON learn a platform for sharing free, open training material for epidemics analysis. This includes a collection of lectures, practicals and case studies, most of which are distributed under CC-BY license.
My earlier research was mostly dedicated to developing multivariate approaches (factorial methods, clustering algorithms), for exploring genetic data. I am still involved in some of these aspects. I am the author of adegenet, a popular R package for genetic/genomic data analysis. With Zhian Kamvar, I am also running a course on data science for population genetic with PR statistics.