Imperial College London


Faculty of MedicineSchool of Public Health

Research Fellow



+44 (0)20 7594 3229a.cori




G27Norfolk PlaceSt Mary's Campus






I am a mathematical and statistical modeller at the Medical Research Council (MRC) Centre for Outbreak Analysis and Modelling. My research interests lie in developing statistical methods to understand the dynamics of epidemics and inform control policies, using a range of data. I have worked on a variety of infections including influenza, SARS, MERS, Ebola, MRSA, poliomyelitis, malaria, and HIV.

When working on acute infections, I am particularly interested in developing methods that can be used in real time, for a broad range of pathogens, to quickly characterise their transmissibility, detect potential changes in transmission patterns over time and space, and predict possible outbreak trajectories under a range of control measures. I am the author of the R package EpiEstim [1], which allows estimating the transmissibility of a pathogen in real time during an outbreak. We used this tool extensively during the West African Ebola outbreak, as part of the WHO Ebola Response Team. This allowed initially demonstrating that this new strain of Ebola had similar transmissibility to previous strains, but that if no control measures were implemented, the number of cases was going to be dramatic [2]. Later in the epidemic, we used EpiEstim to quantify the decrease in transmissibility as control measures were implemented, and to show that regions of high transmissibility were generally regions with high funeral attendance, and slow hospitalisation [3,4,5]. 

The West African Ebola Outbreak has highlighted the need for other tools, to clean outbreak data and analyse it to inform policy in a timely and robust manner [6]. I am currently involved in a range of projects trying to address these gaps, many of which stem from discussions within the R Epidemics CONsortium, which I am a member of.

One of these projects, mRIIDS, which started in early 2017, is a collaboration with Dr. Pierre NouvelletProMED, HealthMap and, for which we were awarded USAID funding, aiming at developing a tool for real-time mapping of disease transmission risk from one country to another. 

I also work on less acute infections, in particular HIV. I have been a member of the mathematical modelling team of the HPTN 071-PopART trial since 2012. This exciting study was designed to test, in several communities in Zambia and South Africa, the potential impact of universal annual home-based HIV testing and early antiretroviral therapy on the HIV incidence at the population level. Mathematical modelling played a major role in the trial design [7], and is being used continuously as the trial is implemented to monitor progress and adjust targets. It will also be a crucial tool at the end of the trial to help analysis of data, interpretation of results, and extrapolation at larger spatial and temporal scale. To that effect, Prof. Christophe Fraser, Dr. Mike Pickles and myself have developed a very fast individual-based model of HIV transmission, tailored to describe in detail the HIV epidemics in the PopART communities, as well as the PopART intervention. Building this model has been challenging, in particular because parameterisation of a number of important components of the model was difficult. Thanks to the very rich data collected as part of the trial, and to collaborations with other HIV studies, we are working our way through parameterising this model. See e.g. [8] for our work on parameterising untreated HIV progression; a number of fascinating analyses of PopART data, on male circumcision, sexual partnerships, etc. are also underway. 

In the future, we plan to apply our fast individual-based HIV model to other contexts; we think the modularity of our code, and the speed of our simulation make it a unique tool to investigate various questions regarding HIV transmission dynamics.

[1] Cori et al. A new framework and software to estimate time-varying reproduction numbers during epidemics. AJE 2013

[2] WHOER Team. Ebola Virus Disease in West Africa — The First 9 Months of the Epidemic and Forward Projections. NEJM 2014

[3] WHOER Team. West African Ebola Epidemic after One Year — Slowing but Not Yet under Control. NEJM 2015

[4] WHOER Team. After Ebola in West Africa : unpredictable risks, preventable epidemics. NEJM 2016

[5] WHOER Team. Exposure patterns driving Ebola transmission in West Africa. PLoS Med 2016

[6] Cori et al. Key data for outbreak evaluation: building on the Ebola experience, Trans. R. Soc. B 2017

[7] Cori et al. HPTN 071 (PopART): a cluster-randomized trial of the population impact of an HIV combination prevention intervention including universal testing and treatment: Mathematical model. PLoS One 2014

[8] Cori et al. CD4 cell dynamics in untreated HIV-1 infection : overall rates, and effects of age, viral load, gender and calendar time. AIDS 2015


I used to be the co-organizer our 2 week course on Mathematical Models of the Epidemiology & Control of Infectious Diseases in 2012, 2013, and 2014.

R packages I have developed or contributed to 

  • EpiEstim, a package to estimate transmissibility during epidemics from incidence data (author)
  • Outbreaker, a package to infer outbreak dynamics from epidemiological and genetic data (contributor)
  • Epibase, a package for storage and basic analysis of outbreaks (contributor)        

Selected Publications

Journal Articles

Valtat S, Cori A, Carrat F, et al., 2011, Age distribution of cases and deaths during the 1889 influenza pandemic, Vaccine, Vol:29, ISSN:0264-410X, Pages:B6-B10

Boelle P-Y, Ansart S, Cori A, et al., 2011, Transmission parameters of the A/H1N1 (2009) influenza virus pandemic: a review, Influenza and Other Respiratory Viruses, Vol:5, ISSN:1750-2640, Pages:306-316

Valleron A-J, Cori A, Valtat S, et al., 2010, Transmissibility and geographic spread of the 1889 influenza pandemic, Proceedings of the National Academy of Sciences of the United States of America, Vol:107, ISSN:0027-8424, Pages:8778-8781

Cori A, Boelle P-Y, Thomas G, et al., 2009, Temporal Variability and Social Heterogeneity in Disease Transmission: The Case of SARS in Hong Kong, PLOS Computational Biology, Vol:5, ISSN:1553-734X

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