My research involves a variety of emerging infectious diseases in animal and human populations, with a constant focus on the development of mathematical models to characterise their epidemiology and to evaluate control strategies.
I currently hold a Junior Research Fellowship funded by Imperial College to investigate the within-host dynamics of dengue virus pathogenesis and the human antibody response. My current research involves applying machine learning techniques to clinical trial data to model the serotype-specific antibody responses and their complex interactions following natural infection and vaccination.
I am particularly interested in developing mathematical models to characterise the transmissibility of dengue virus and the complex antibody patterns arising upon natural exposure and vaccination. My analysis of the clinical trials data of the Sanofi-Pasteur dengue vaccine brought to light the fundamental role that pre-exposure to dengue before vaccination has on immunogenicity and protection against dengue. This finding laid the foundations for further modelling studies, such as predicting the long-term benefits and risks of the Sanofi-Pasteur dengue vaccine, which was published in Science in 2016.
My research also focuses on the real-time analysis of epidemic data from novel emerging infections to improve situational awareness. As a member of the WHO Ebola Response Team, my research has contributed to the epidemiological characterisation of the Ebola virus affecting West Africa in 2013-2015 and has informed response planning at the WHO. More recently, following the spread of Zika virus in Latin America in 2015-2016, I have worked towards improving the characterisation of Zika virus epidemiology, life-history and transmissibility, with particular focus on the public health implications and future priorities for Zika control. This study was published in Science in 2016.
Finally, I am interested in developing models to integrate data streams from multiple sources and in addressing the statistical challenges posed by surveillance data. In my work published in PNAS in 2013, I developed a novel modelling approach integrating syndromic, virological and serological data, which I applied to analyse the complex epidemic patterns (3 waves of infection) caused by the 2009 H1N1 pandemic virus in England between 2009 and 2011.
et al., 2018, Refined efficacy estimates of the Sanofi Pasteur dengue vaccine CYD-TDV using machine learning, Nature Communications, Vol:9, ISSN:2041-1723
et al., 2017, Using Wolbachia for Dengue Control: Insights from Modelling., Trends in Parasitology, Vol:34, ISSN:1471-5007, Pages:102-113
et al., 2017, International risk of yellow fever spread from the ongoing outbreak in Brazil, December 2016 to May 2017, Eurosurveillance, Vol:22, ISSN:1560-7917, Pages:1-4
et al., 2016, Benefits and risks of the Sanofi-Pasteur dengue vaccine: Modeling optimal deployment, Science, Vol:353, ISSN:0036-8075, Pages:1033-1036
et al., 2016, Countering the Zika epidemic in Latin America, Science, Vol:353, ISSN:1095-9203, Pages:353-354
et al., 2015, Ebola virus disease among children in West Africa, New England Journal of Medicine, Vol:372, ISSN:1533-4406, Pages:1274-1277
Dorigatti I, Cauchemez S, Ferguson NM, 2013, Increased transmissibility explains the third wave of infection by the 2009 H1N1 pandemic virus in England, Proceedings of the National Academy of Sciences of the United States of America, Vol:110, ISSN:0027-8424, Pages:13422-13427