Professor Christl Donnelly
CBE FMedSci FRS
Professor of Applied Statistics
My research programme brings together and develops statistical and biomathematical methods to analyse epidemiological patterns of infectious diseases. I have studied a variety of diseases, with a particular interest in outbreaks. I also have interests in ecology, conservation and animal welfare.
I use rigorous parameter estimation and hypothesis testing to gain the robust insights from dynamical models of disease transmission, demography and interventions. My research programme aims to improve our understanding of (and ability to predict) the effect of interventions on infectious agent transmission dynamics and population structure. The ultimate goal is to make control strategies as effective as they can be.
I have studied many infectious diseases, including Zika virus, Ebola, MERS, influenza, SARS, bovine TB, foot-and-mouth disease, rabies, cholera, dengue, BSE/vCJD, malaria and HIV/AIDS. I was a leading member of the WHO Ebola Response Team (2014-2016). I was also deputy chair of the Independent Scientific Group on Cattle TB (1998-2007) which designed, oversaw and analysed the Randomised Badger Culling Trial.
I studied mathematics as an undergraduate at Oberlin College and biostatistics as a graduate student at Harvard School of Public Health.
A quantitative framework to define the end of an outbreak: application to Ebola Virus Disease
Djaafara BA. et al, (2020)
Spatiotemporal variability in case fatality ratios for the 2013-2016 Ebola epidemic in West Africa.
Forna A. et al, (2020), International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases, 93, 48 - 55
Risk of yellow fever virus importation into the United States from Brazil, outbreak years 2016–2017 and 2017–2018
Dorigatti I. et al, (2019), Scientific Reports
Rabies virus neutralising antibodies in healthy, unvaccinated individuals: What do they mean for rabies epidemiology?
Gold S. et al, (2019), PLoS Neglected Tropical Diseases
Optimising Renewal Models for Real-Time Epidemic Prediction and Estimation
Parag KV. and Donnelly CA., (2019)