Professor Christl Donnelly
Contact information
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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Determining herd immunity thresholds for hepatitis A virus transmission to inform vaccination strategies among people who inject drugs in 16 U.S. States
Journal article
Yang J. et al, (2023), Clinical Infectious DIseases
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Real-time RT-PCR for Venezuelan equine encephalitis complex, Madariaga and Eastern equine encephalitis viruses: application in human and mosquito public health surveillance in Panama
Journal article
Carrera J-P. et al, (2023), Journal of Clinical Microbiology
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Madariaga and Venezuelan equine encephalitis virus seroprevalence in rodent enzootic hosts in Eastern and Western Panama.
Journal article
Carrera J-P. et al, (2023), bioRxiv
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Strategic use of SARS-CoV-2 wastewater concentration data could enhance, but not replace, high-resolution community prevalence survey programmes
Preprint
Mills C. et al, (2023)
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Improving estimates of epidemiological quantities by combining reported cases with wastewater data: a statistical framework with applications to COVID-19 in Aotearoa New Zealand
Preprint
Watson LM. et al, (2023)