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Cunniffe Group: Using citrus as a model for scaling control of disease at local scales to strategies for regional control


Supervisor: Nik Cunniffe (Plant Sciences)


Citrus production in the United States, Brazil and worldwide is threatened by a number of exotic pathogens, most notably citrus canker and citrus greening. Due to intensive research interest and particularly the availability of good epidemiological data, most notably detailed disease surveys at both local and regional scales, citrus is an excellent model system for food security threats to agricultural and horticultural crops more generally. A number of recent models of citrus diseases have targeted spread at scales relevant to individual producers, often tracking the disease status of individual plants within a planting (e.g. Cunniffe et al. (2015)). However, these models can be extended in a number of ways, such as more faithfully representing the effects of control strategies, accounting for within-host pathogen dynamics, including environmental drivers, accounting for selection pressures caused by preferential removal of symptomatic hosts, and, for citrus greening, vector dynamics. In practice, success of control in protecting threatened regions will depend on matching the temporal and spatial scales of control with the inherent scales of pathogen and vector populations. However, models of large-scale spread dynamics have not yet been developed, although such models have been developed for forest pathogens (Cunniffe et al., 2016). Scaling-up smaller scale models to track spread at spatial scales would allow landscape-scale control to be understood and assessed. This would have relevance to a number of disease, not just citrus.


The student would learn i) mapping a complex biological system to a parsimonious mathematical model; ii) modern methods for simulating epidemiological models; iii) high performance computing, via use of the a HPC cluster; iv) experience of applying mathematics to a biological system.
The project would best suit a student with from a background in mathematics, engineering, physics or ecology, ideally with some knowledge of computer programming. However, students with a wet-lab background have enjoyed and been successful in my laboratory in the past.


  • Cunniffe, N.J., Stutt, R.O.J.H., DeSimone, R.E., Gottwald, T.R. and Gilligan, C.A. (2015) Optimising and communicating options for the control of invasive plant disease when there is epidemiological uncertainty. PLoS Computational Biology. 11:e1004211.
  • Cunniffe, N.J., Cobb, R.C., Meentemeyer, R.K., Rizzo, D.R. and Gilligan, C.A. (2016) Modeling when, where and how to manage a forest epidemic, motivated by sudden oak death in California. Proceedings of the National Academy of Sciences. 113:5640-5645.

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