Imperial College London


Faculty of MedicineSchool of Public Health




+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 Global Infectious Disease Analysis. 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 MERS, influenza, SARS-CoV-1, HIV, Ebola, and most recently SARS-CoV-2.

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 [1,2], which allows estimating the transmissibility of a pathogen in real time during an outbreak. We used this tool extensively during the 2013-16 West African Ebola epidemic but also in the more recent Ebola outbreaks in DRC and Guinea, to quantify the transmissibility at various points in the outbreak, forecast future incidence, and assess the impact of control measures [3-7]. EpiEstim has become particularly prominent during the COVID-19 pandemic and is being widely used by academics, public health agencies and governments worldwide to monitor SARS-CoV-2 transmission. 

Recent epidemics and the ongoing pandemic have highlighted the need for other tools, to clean outbreak data and analyse it to inform policy in a timely and robust manner [8,9]. I am currently involved in a range of projects trying to address these gaps. One of these projects, mRIIDS, is a collaboration with Dr. Pierre NouvelletProMED, HealthMap and, aiming at developing a tool for real-time mapping of disease transmission risk from one country to another [10].

As many colleagues, my research in the last year has focused primarily on COVID-19. I was involved in early efforts to characterise the magnitude of the epidemic as well as quantifying transmissibility and severity of SARS-CoV-2 [11, 12]. I contributed to work estimating the impact of interventions against COVID-19 [13-15] and examining the issue of nosocomial SARS-CoV-2 transmission [16]. Most recently, I co-led efforts to characterise the epidemic in England, and to assist the design, monitoring and evaluation of the "roadmap out of lockdown" [17,18]. 

Ongoing projects include extensions to EpiEstim and epidemic forecasting approaches with a particular focus on spatial spread of infectious diseases; as well as use of genetic and epidemiological data to better understand and control nosocomial outbreaks. 

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

[2] Thompson et al. Improved inference of time-varying reproduction numbers during infectious disease outbreaks, Epidemics 2019

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

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

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

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

[7] EOE Team. Outbreak of Ebola Virus Disease in the Democratic Republic of the Congo, April-May 2018: an epidemiological study. The Lancet 2018

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

[9] Polonsky et al. Outbreak analytics: a developing data science for informing the response to emerging pathogens. Phil Trans R Soc B 2018

[10] Bhatia et al. Using digital surveillance tools for near real-time mapping of the risk of international infectious disease spread: Ebola as a case study. NPJ Digital Medicine 2021

[11] Bhatia et al. Estimating the number of undetected COVID-19 cases among travellers from mainland China. Wellcome Open Research 2020

[12] Verity et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet ID 2020

[13] Nouvellet P et al. Reduction in mobility and COVID-19 transmission. Nature communication 2020

[14] Mishra S et al. Comparing the responses of the UK, Sweden and Denmark to COVID-19 using counterfactual modelling Scientific reports 2021

[15] Ragonnet-Cronin M et al. Genetic evidence for the association between COVID-19 epidemic severity and timing of non-pharmaceutical interventions. Nature communication 2021

[16] Abbas M et al. Explosive nosocomial outbreak of SARS-CoV-2 in a rehabilitation clinic: the limits of genomics for outbreak reconstruction. Journal of Hospital Infection 2021

[17] Knock et al. The 2020 SARS-CoV-2 epidemic in England: key epidemiological drivers and impact of interventions. Science Translational Medicine 2021

[18] Sonabend et al. Non-pharmaceutical interventions, vaccination and the Delta variant: epidemiological insights from modelling England's COVID-19 roadmap out of lockdown – a mathematical modelling study. The Lancet 2021

Selected Publications

Journal Articles

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

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

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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