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Please use this identifier to cite or link to this item: http://hdl.handle.net/10373/1082

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Title: Applications of percolation theory to fungal spread with synergy
Authors: Ludlam, Jonathan J.
Gibson, Gavin J.
Otten, Wilfred
Gilligan, Christopher A.
Affiliation: University of Abertay Dundee. Scottish Informatics, Mathematics, Biology and Statistics Centre
Keywords: Percolation
Synergistic interactions
Saprotrophic invasion
Microcosm experiments
Bayesian
Markov chain methods
Issue Date: May-2012
Publisher: The Royal Society
Type: Journal Article
Refereed: peer-reviewed
Rights: This is the author's final version of this article. Published version (c)The Royal Society, available from http://dx.doi.org/10.1098/​rsif.2011.0506
Citation: Ludlam, J.J., et al. 2012. Applications of percolation theory to fungal spread with synergy. Journal of the Royal Society Interface. 9(70): pp.949-956. Available from http://dx.doi.org/10.1098/​rsif.2011.0506
Abstract: There is increasing interest in the use of the percolation paradigm to analyze and predict the progress of disease spreading in spatially-structured populations of animals and plants. The wider utility of the approach has been limited, however, by several restrictive assumptions, foremost of which is a strict requirement for simple nearest-neighbour transmission, in which the disease history of an individual is in uenced only by that of its neighbours. In a recent paper the percolation paradigm has been generalised to incorporate synergistic interactions in host infectivity and susceptibility and the impact of these interactions on the invasive dynamics of an epidemic has been demonstrated. In the current paper we elicit evidence that such synergistic interactions may underlie transmission dynamics in real-world systems by rst formulating a model for the spread of a ubiquitous parasitic and saprotrophic fungus through replicated populations of nutrient sites and subsequently tting and testing the model using data from experimental microcosms. Using Bayesian computational methods for model tting, we demonstrate that synergistic interactions are necessary to explain the dynamics observed in the replicate experiments. The broader implications of this work in identifying disease control strategies that de ect epidemics from invasive to non-invasive regimes are discussed.
URI: http://hdl.handle.net/10373/1082
ISSN: 1742-5689
Appears in Collections:SIMBIOS Collection
Science Engineering & Technology Collection

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