Dr Ilaria Dorigatti
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.
Ilaria currently holds a Junior Research Fellowship funded by Imperial College to investigate the within-host dynamics of dengue virus pathogenesis and the human antibody response. Her 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.
Ilaria is 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.
Her 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, her 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, she has 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.
Finally, Dr Dorigatti is interested in developing models to integrate data streams from multiple sources and in addressing the statistical challenges posed by surveillance data.