Dr Nik Cunniffe
Theoretical and computational epidemiology
I use mathematical analysis and computer simulations to model disease epidemics in crops and plants. Modelling promotes an understanding of why some pathogens invade, whereas populations of other pathogens decline; why some pathogens persist over long time scales, whereas others die out very quickly; and why some pathogens spread quickly through regions, countries or even continents, whereas others cause only localised outbreaks. This epidemiological understanding can be used to determine how best to perturb host-pathogen systems to the detriment of the pathogen, and so to design efficient and cost-effective control strategies.
I have a particular interest in analysing simple, tractable, and parsimonious models to reveal the (often unexpected) effects of particular biological mechanisms on disease spread. Examples include host growth, small-scale movement of pathogens through the soil, seasonal disturbances due to cropping cycles, and interactions with agents of biological control; each of these has a profound effect upon the epidemiological dynamics. However, I have also recently been involved in developing efficient, large-scale, spatially-explicit, stochastic models of particular diseases. These models can be used to accurately predict the risk of a particular disease in a given region and/or to determine the likely effect of a proposed control strategy, together with its risk of failure.
Figure descriptions:
Figure 1: Future risk of Sudden Oak Death in California. Sudden Oak Death, caused by Phytophthora ramorum (a fungal-like organism from the same genus as potato late blight, cause of the Irish Potato Famine), has already killed tens of thousands of oaks in California, and if left unchecked will potentially kill many more.
Figure 2: Control of Citrus Canker (caused by the bacterium Xanthomonas axonopodis) in urban Florida controversially involved cutting down all trees within a certain radius of symptomatic infected trees. Modelling allows an optimal radius of control to be determined, balancing preventing the spread of the pathogen against killing healthy hosts.
Figure 3: Theoretical models often reveal counter-intuitive results. For soil-borne root pathogens, models reveal that an interaction with the growth of the host plant means that increasing the amount of control can lead to a paradoxical increase in the prevalence of disease.
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Department of Plant Sciences
University of Cambridge
Downing Site Downing Street
CAMBRIDGE CB2 3EA
Email: Nik.Cunniffe@plantsci.cam.ac.uk
Office +44 (0)1223 333954
