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.
Population antibody responses following COVID-19 vaccination in 212,102 individuals
Ward H. et al, (2022), Nature Communications, 13
Estimating Zika virus attack rates and risk of Zika virus-associated neurological complications in Colombian capital cities with a Bayesian model
Charniga K. et al, (2022), Royal Society Open Science
Trends in SARS-CoV-2 infection prevalence during England's roadmap out of lockdown, January to July 2021.
Eales O. et al, (2022), PLoS computational biology, 18
Variant-specific symptoms of COVID-19 in a study of 1,542,510 adults in England
Whitaker M. et al, (2022), Nature Communications
Structural identifiability of compartmental models for infectious disease transmission is influenced by data type
Dankwa EA. et al, (2022), Epidemics: the journal of infectious disease dynamics