My research focus on four streams of work, vaccine policy research, the development of novel methodological tools, integration of serological data into models to understand transmission and real-time modelling and outbreak response. I have also 10 years of experience working in a public health institution (Public Health England).
Real-time modelling and outbreak response I have a keen interest in the particular problem of real-time modelling for outbreak response. This includes understanding the sources of uncertainties and including these in the inference methods, and designing fast and efficient models to inform policy makers. As part of the modelling team for PHE (then HPA), I built in real-time the model that informed the UK Scientific Advisory Group for Emergencies and the JCVI on vaccination strategy during the 2009 pandemic in the UK and was part of the modelling taskforce at the London School of Hygiene and Tropical Medicine during the 2014 Ebola crisis in West Africa. I am now fully committed to provide modelling support for the COVID-19 outbreak response.
Vaccine policy research I developed transmission models and cost-effectiveness analyses to support policy makers in decisions regarding changes to immunization programmes. For the last 10 years, I have been a regular contributor to the Joint Committee on Vaccination and Immunisation in the UK. In particular I have developed models and undertaken the cost-effectiveness analyses supporting the vaccination of pregnant women against seasonal influenza, the introduction of paediatric influenza vaccination in children, quadrivalent influenza vaccines and assessing the impact of the new paediatric arm of the flu immunisation programme on the pre-existing elderly arm. I have also lead studies on the cost-effectiveness of influenza and high dose influenza vaccines.
Development of novel methodological tools I am driven to improve existing methodological tools whenever possible in particular to fully exploit new data streams. In the past years I have developed with various academic collaborators novel methods to improve the inference of transmission model parameters using surveillance data. With collaborators at the London School of Economics I have developed a method to include a diffusion process to represent time varying quantities when mechanisms of change are difficult to assess, co-developed two R packages, one for handling jointly genetic and epidemiological information, and the second for performing evidence synthesis and cost-effectiveness from influenza surveillance data.
Integration of serological data into models to understand transmission As a result of my interactions with epidemiologists and virologists, I have developed a strong interest in using biological markers (in particular antibody titres from sera) to better understand pathogens transmission. I have developed a framework to derive infection incidence from sequential serology while integrating potential additional data sources, a new type of model including the dynamics of titre changes alongside the dynamics of transmission. During the 2014 Ebola crisis in West Africa, I have developed a household serological survey in the Democratic Republic of Congo to assess asymptomatic transmission. The resulting data were analysed using a Bayesian mixture model. The protocol was then adapted to survey returning healthcare workers, estimate potential asymptomatic infection rate and link it with risk while in the field.
et al., 2021, Key epidemiological drivers and impact of interventions in the 2020 SARS-CoV-2 epidemic in England, Science Translational Medicine, ISSN:1946-6234
Opatowski L, Baguelin M, Eggo RM, 2018, Influenza interaction with cocirculating pathogens and its impact on surveillance, pathogenesis, and epidemic profile: A key role for mathematical modelling, PLOS Pathogens, Vol:14, ISSN:1553-7366
et al., 2013, Assessing Optimal Target Populations for Influenza Vaccination Programmes: An Evidence Synthesis and Modelling Study, PLOS Medicine, Vol:10, ISSN:1549-1277
Dureau J, Kalogeropoulos K, Baguelin M, 2013, Capturing the time-varying drivers of an epidemic using stochastic dynamical systems, Biostatistics, Vol:14, ISSN:1465-4644, Pages:541-555
et al., 2010, Vaccination against pandemic influenza A/H1N1v in England: A real-time economic evaluation, Vaccine, Vol:28, ISSN:0264-410X, Pages:2370-2384
et al., 2020, Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand