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|Title: ||Inferring the dynamics of a spatial epidemic from time-series data|
|Authors: ||Filipe, J. A. N.|
Gibson, G. J.
Gilligan, C. A.
|Affiliation: ||University of Abertay Dundee. Scottish Informatics, Mathematics, Biology and Statistics Centre|
|Keywords: ||Rhizoctonia solani|
|Issue Date: ||Mar-2004|
|Publisher: ||Springer Verlag|
|Type: ||Journal Article|
|Rights: ||Published version (c)Springer Verlag, available from DOI: 10.1016/j.bulm.2003.09.002. The original publication is available at www.springerlink.com|
|Citation: ||Filipe, J.A.N., et al. 2004. Inferring the dynamics of a spatial epidemic from time-series data. Bulletin of Mathematical Biology. 66(2): pp.373-391. Available from http://dx.doi.org/10.1016/j.bulm.2003.09.002|
|Abstract: ||Spatial interactions are key determinants in the dynamics of many epidemiological and ecological systems; therefore it is important to use spatio-temporal models to estimate essential parameters. However, spatially-explicit data sets are rarely available; moreover, fitting spatially-explicit models to such data can be technically demanding and computationally intensive. Thus non-spatial models are often used to estimate parameters from temporal data. We introduce a method for fitting models to temporal data in order to estimate parameters which characterise spatial epidemics. The method uses semi-spatial models and pair approximation to take explicit account of spatial clustering of disease without requiring spatial data. The approach is demonstrated for data from experiments with plant populations invaded by a common soilborne fungus, Rhizoctonia solani. Model inferences concerning the number of sources of disease and primary and secondary infections are tested against independent measures from spatio-temporal data. The applicability of the method to a wide range of host-pathogen systems is discussed.|
|Appears in Collections:||SIMBIOS Collection|
Science Engineering & Technology Collection
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