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Department of Plant Sciences

 

MRC / UF / OTHER

Supervisors: Dr Nik Cunniffe

 

Project Summary

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, by including 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, detailed models of large-scale spread dynamics have not yet been developed, although such models have been developed for forest pathogens (Cunniffe et al., 2016), and initial work shows how simple models can be used for surveillance (Mastin et al., 2020). Scaling-up smaller scale models to track spread at spatial scales would allow landscape-scale control to be understood and assessed. There are also opportunities to use recent advances in model predictive control to emulate complex models by simpler proxies, allowing the mathematical machinery of optimal control theory to unambiguously determine which control strategies are most likely to be successful (Bussell et al., 2019). This would have relevance to a number of diseases, including those of human and animal diseases, not just citrus.

 

References

  • Cunniffe, N.J., Stutt, R., DeSimone, 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 (4). doi:10.1371/journal.pcbi.1004211
  • Cunniffe, N.J., Cobb, R.C., Meentemeyer, R.K., Rizzo, D.M. 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 (PNAS). 113 (20), 5640-5645. doi:10.1073/pnas.1602153113
  • Mastin, A.J., Gottwald, T.R, van den Bosch, F. Cunniffe, N.J. and Parnell, S.R. (2020) Optimising Risk-based Surveillance for Early Detection of Invasive Plant Pathogens, PLOS Biology 18 (10). doi:10.1371/journal.pbio.3000863
  • Bussell, E.H., Dangerfield, C.E., Gilligan, C.A. and Cunniffe, N.J. (2019) Applying Optimal Control Theory to Complex Epidemiological Models to Inform Real-world Disease Management, Philosophical Transactions of the Royal Society, 374 (1776). doi:10.1098/rstb.2018.0284