### Abstract

Original language | English |
---|---|

Pages (from-to) | 373-391 |

Number of pages | 19 |

Journal | Bulletin of Mathematical Biology |

Volume | 66 |

Issue number | 2 |

DOIs | |

State | Published - Mar 2004 |

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*Bulletin of Mathematical Biology*,

*66*(2), 373-391. DOI: 10.1016/j.bulm.2003.09.002

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*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.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Inferring the dynamics of a spatial epidemic from time-series data

AU - Filipe,J. A. N.

AU - Otten,Wilfred

AU - Gibson,Gavin J.

AU - Gilligan,Christopher A.

PY - 2004/3

Y1 - 2004/3

N2 - 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.

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.

U2 - 10.1016/j.bulm.2003.09.002

DO - 10.1016/j.bulm.2003.09.002

M3 - Article

VL - 66

SP - 373

EP - 391

JO - Bulletin of Mathematical Biology

T2 - Bulletin of Mathematical Biology

JF - Bulletin of Mathematical Biology

SN - 0092-8240

IS - 2

ER -