My main research interest is to improve understanding of the way pathogens spread in human and animal populations. In particular, I am interested in the development of sound methods for the analysis of infectious disease data, with the aim to get insights on dynamics of infection and transmission and to support decision making.
From November 2013, after 8 amazing years in the Department of Infectious Disease Epidemiology at Imperial College London, I will move to Institut Pasteur (Paris) to head a new Mathematical Modelling of Infectious Diseases Unit.
Early risk assessment for emerging infectious disease (EID)
Risk assessment for EIDs like 2009 pandemic influenza or Middle East Respiratory Syndrome coronavirus (MERS-CoV) is challenging because surveillance data are often scarce and biased (e.g. only a small proportion of cases are detected; severe cases are more likely to be detected than others). Besides, transmissibility is difficult to assess because chains of transmission are typically unobserved. I am interested in the development of methods that can be used to overcome these problems.
Recently, for example, in collaboration with colleagues in the Department, we have analysed all publically available epidemiologic and genetic data to ascertain under-reporting and selection bias in the ongoing epidemic of MERS-CoV, as well as the transmission scenario for this virus.
In another work stream on zoonotic viruses (i.e. viruses that circulate in an animal reservoir but can “spill-over” into humans), I showed it was possible to quantify the epidemic potential of the virus simply from animal exposure data (PLoS Med, 2013). The approach was used to support CDC in characterising an emerging swine-origin A/H3N2 influenza variant. In the past, I have worked on techniques that can be used in real-time to monitor the efficacy of control measures from epidemic time series (AJE 2006; EID, 2006); and with Anne Cori, we have recently improved these methods in simple software for field epidemiologists (Cori et al, AJE, in press).
In 2009, working closely with CDC and other institutions, I was strongly involved in the response to the 2009 influenza pandemic (NEJM 2009; Fraser et al, Science 2009; Yu et al, EID, 2012).
Analysis of epidemic data - Investigating the determinants of transmission
I am interested in the analysis of epidemiological data where members of small communities (e.g. households or schools) are followed-up during an epidemic. This type of data can provide unique insights on the determinants of transmission; but analysis is challenging because it is rarely possible to determine exactly who got infected by whom. I rely on sophisticated statistical methods to probabilistically reconstruct the pattern of spread and estimate the rates of transmission in different settings. I have ongoing collaborations looking at different aspects of influenza transmission in households in Vietnam, Hong Kong and Bangladesh; as well as unique transmission challenge study on volunteers (Nottingham). In the past, I used similar methods to study how social networks shaped the spread of influenza in schools (PNAS, 2011), characterize influenza transmission in households (NEJM, 2009; Nature, 2005; Stat Med, 2004) or to study heterogeneity in S. pneumoniae transmission in schools (JASA, 2006).
CHARACTERISING EPIDEMIC DYNAMICS and the impact of interventions
In 2008, I evaluated the impact of school closure on influenza transmission from the analysis of surveillance Sentinel data and the timing of holidays in France (Nature, 2008). It was an opportunity to develop new statistical methods, based on Sequential Monte Carlo techniques, to fit highly structured epidemic models to aggregated data. More recently, with Ilaria Dorigatti and Neil Ferguson, we analysed syndromic, virological and serological data collected in England in 2009-2011 to understand why an unexpectedly large third wave of pandemic influenza occurred in the UK (Dorigatti et al, PNAS, 2013).
Advances in Sequential Monte Carlo methodology offer new opportunities to fit complex mathematical model to infectious disease data and I am keen to do more research in this area.
MOVING TO INSTITUT PASTEUR
I am very excited about my move to Institut Pasteur. With 130 research laboratories and units, 20 National Reference Centres (in charge of disease surveillance in France) and a unique international network of 32 Institutes that spread across the five continents, Institut Pasteur is a unique environment to develop inter-disciplinary and translational research. This very diverse set of outstanding competences offers a unique opportunity to develop a multidisciplinary perspective on infectious disease modelling and construct the next generation of mathematic models that make best use of data gathered at very different scales (cellular, host, population). My Unit will also provide real-time analyses and support to decision makers during emerging infectious disease outbreaks.
et al., 2016, Model-Based Comprehensive Analysis of School Closure Policies for Mitigating Influenza Epidemics and Pandemics, Plos Computational Biology, Vol:12, ISSN:1553-734X
et al., 2016, The environmental deposition of influenza virus from patients infected with influenza A(H1N1)pdm09: Implications for infection prevention and control, Journal of Infection and Public Health, Vol:9, ISSN:1876-0341, Pages:278-288
et al., 2015, Use of Viremia to Evaluate the Baseline Case Fatality Ratio of Ebola Virus Disease and Inform Treatment Studies: A Retrospective Cohort Study, Plos Medicine, Vol:12, ISSN:1549-1676
et al., 2015, Chains of transmission and control of Ebola virus disease in Conakry, Guinea, in 2014: an observational study, Lancet Infectious Diseases, Vol:15, ISSN:1473-3099, Pages:320-326
et al., 2015, A Change in Vaccine Efficacy and Duration of Protection Explains Recent Rises in Pertussis Incidence in the United States, Plos Computational Biology, Vol:11, ISSN:1553-734X