Head of Group: Dr Nik Cunniffe
We use mathematical analysis and computer simulations to understand the spread and control of plant and tree diseases. Our theoretical work attempts to isolate the ways in which factors including host growth, host topography, pathogen dispersal, asymptomatic infection and biological control affect the pattern of spread. However, we have also been involved in developing large-scale, spatially-explicit, stochastic, simulation models that can be fitted to data on the real-world spread of pathogens of current regulatory concern. Examples include sudden oak death, Chalara ash dieback, Dutch elm disease, citrus canker and huanglongbing. This type of model can be used to accurately predict the risk of disease in a given region and/or to quantify the likely effect of any proposed control strategy, together with its inherent risk of failure.
Current projects include:
Landscape scale epidemiological modeling
Plant pathogens spread at the scale of countries or even continents, and models must reflect this. Working at these scales requires efficient computation, and careful use of (often patchy) spread data. We work on dynamic simulation models of a number of pathogens, including diseases of both trees and crops.
Modelling biological control
Biological control uses a natural enemy of a pathogen to effect a reduction in the level of disease. There are obvious attractions. However, biological control has all too often either failed to work or has proved too unreliable to be a realistic proposition. We are using mathematical modelling to understand how this can be improved.
Dutch elm disease in East Sussex
The only remaining significant population of mature elm trees in England is centred on East Sussex. We are working with East Sussex council to use epidemiological modelling to understand how this important natural resource can best be preserved in the light of an apparent resurgence in the number of new infections.
Optimising eradication via localised removal
Eradication of invasive pathogens can be attempted via local removal of hosts, in which all hosts within a particular distance of detected infected hosts are removed. However, the correct distance depends on a complex interplay between the epidemiology of the plant-pathogen interaction, the current state of the epidemic and the logistics of detection and control. We have developed an interactive interface, available at http://www.webidemics.com/, designed to illustrate these issues to an audience of stakeholders and policy makers.