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.
Identifying Counties at Risk of High Overdose Mortality Burden Throughout the Emerging Fentanyl Epidemic in the United States: A Predictive Statistical Modeling Study
Marks C. et al, (2021), Lancet Public Health
Prevalence and associated factors with mental health outcomes among interns and residents physicians during COVID-19 epidemic in Panama: a cross-sectional study
Espinosa-Guerra EA. et al, (2021)
Prevalence of antibody positivity to SARS-CoV-2 following the first peak of infection in England: serial cross-sectional studies of 365,000 adults
Ward H. et al, (2021), The Lancet Regional Health - Europe
Better educational signage could reduce disturbance of resting dolphins
Donnelly R. et al, (2021), PLoS One
Genetic evidence for the association between COVID-19 epidemic severity and timing of non-pharmaceutical interventions
Ragonnet-Cronin M. et al, (2021), Nature Communications