Inferring the dynamics of a spatial epidemic from time-series data

J. A. N. Filipe, Wilfred Otten, Gavin J. Gibson, Christopher A. Gilligan

Research output: Contribution to journalArticle

  • 13 Citations

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.
Original languageEnglish
Pages (from-to)373-391
Number of pages19
JournalBulletin of Mathematical Biology
Volume66
Issue number2
DOIs
StatePublished - Mar 2004

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Anthralin
Estimate
Model
methodology
Supravalvular Aortic Stenosis
Spontaneous Fractures
Rhizoctonia
Coinfection
Ecosystem
Cluster Analysis
Fungi
Datasets
Pair approximation
Spatial clustering
Spatio-temporal model
Spatio-temporal data
Model fitting
Spatial model
Spatial data
Time series data

Cite this

Filipe, J. A. N., Otten, W., Gibson, G. J., & Gilligan, C. A. (2004). Inferring the dynamics of a spatial epidemic from time-series data. Bulletin of Mathematical Biology, 66(2), 373-391. DOI: 10.1016/j.bulm.2003.09.002

Filipe, J. A. N.; Otten, Wilfred; Gibson, Gavin J.; Gilligan, Christopher A. / Inferring the dynamics of a spatial epidemic from time-series data.

In: Bulletin of Mathematical Biology, Vol. 66, No. 2, 03.2004, p. 373-391.

Research output: Contribution to journalArticle

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Filipe, JAN, Otten, W, Gibson, GJ & Gilligan, CA 2004, 'Inferring the dynamics of a spatial epidemic from time-series data' Bulletin of Mathematical Biology, vol 66, no. 2, pp. 373-391. DOI: 10.1016/j.bulm.2003.09.002

Inferring the dynamics of a spatial epidemic from time-series data. / Filipe, J. A. N.; Otten, Wilfred; Gibson, Gavin J.; Gilligan, Christopher A.

In: Bulletin of Mathematical Biology, Vol. 66, No. 2, 03.2004, p. 373-391.

Research output: Contribution to journalArticle

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AB - 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.

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Filipe JAN, Otten W, Gibson GJ, Gilligan CA. Inferring the dynamics of a spatial epidemic from time-series data. Bulletin of Mathematical Biology. 2004 Mar;66(2):373-391. Available from, DOI: 10.1016/j.bulm.2003.09.002