Professor Chris Gilligan, Professor of Mathematical Biology
Epidemiology and Modelling
Our research is concerned principally with analysis of factors that influence and control invasion, persistence and variability of disease and other organisms using a combination of experimentation and modelling. We have a lively mixture of experimenters, mathematicians and statisticians working closely on a range of epidemiological and ecological problems.
Our current experimental programme studies the spatial and temporal dynamics of beneficial and pathogenic, soil-borne, micro-organisms at a range of spatial scales and as model experimental microcosms of larger-scale systems in the field. Applications include: interpreting and minimising the risk of failure of biological and chemical control; formulation and testing models for soil biodiversity and saprotrophic dynamics; and use of microcosm experiments as experimental analogues of large field systems.
Our theoretical work uses a range of mathematical and statistical techniques to analyse the spatial and temporal dynamics of epidemics. We are particularly concerned to link theory with data from experiments and observational studies in order to test models that will be used to help in controlling epidemics. While most of the work is focused on plant disease in agricultural, horticultural, plantation and natural environments, our theories extend to other systems. Topics for study include the control of plant disease to minimise risk of resistance breakdown; control of antibiotic and pesticide resistance for plant and animal diseases; spread of genetically modified crops; and invasion of new or recurrent animal and plant diseases.
- Quick Links:
- PhD Studentships
- Current and recent sponsors:
- BBSRC
- DEFRA
- NERC
- USDA
- Wellcome Trust
- National Science Foundation
- USDA Forest Service
- Contact us:
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Department of Plant Sciences
University of Cambridge
Downing Site Downing Street
CAMBRIDGE CB2 3EA
Email: cag1@cam.ac.uk
Office +44 (0)1223 333904
Lab +44 (0)1223 330229
Model prediction for control of rhizomania disease of sugarbeet. Red represents a high probability of disease, green a low probability, white uninfested.