Professor Christl Donnelly
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Colleges
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.
Recent publications
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Clinical and epidemiological characteristics of Madariaga and Venezuelan equine encephalitis virus infections in Panama
Rivera LF. et al, (2024), Emerging Infectious Diseases
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Strategies to increase response rate and reduce non-response bias in population health research: a series of randomised controlled trials during a large COVID-19 study
Atchison CJ. et al, (2024), JMIR Public Health and Surveillance
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Can biosecurity on farms reduce bovine tuberculosis risks in cattle in England? A review of observational and literature-based evidence
Voller C. et al, (2024), Veterinary Record
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COVID-19 testing and reporting behaviours in England across different sociodemographic groups: a population-based study using testing data and data from community prevalence surveillance surveys.
Bajaj S. et al, (2024), The Lancet. Digital health, 6, e778 - e790
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Interdisciplinary modelling and forecasting of dengue
Mills C. et al, (2024)