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.
Leveraging community mortality indicators to infer COVID-19 mortality and transmission dynamics in Damascus, Syria
Watson OJ. et al, (2021), Nature Communications, 12
Modelling the influence of naturally acquired immunity from subclinical infection on outbreak dynamics and persistence of rabies in domestic dogs
Gold S. et al, (2021), PLoS Neglected Tropical Diseases
Vaccine uptake and SARS-CoV-2 antibody prevalence among 207,337 adults during May 2021 in England: REACT-2 study
Ward H. et al, (2021)
Spatial and temporal invasion dynamics of the 2014–2017 Zika and chikungunya epidemics in Colombia
Charniga K. et al, (2021), PLOS Computational Biology, 17, e1009174 - e1009174
COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study.
ISARIC Clinical Characterisation Group None., (2021), Infection