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Estimating the kernel parameters of premises-based stochastic models of farmed animal infectious disease epidemics using limited, incomplete, or ongoing data
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Rorres, Chris, Pelletier, Sky T. K., Keeling, Matthew James and Smith, Gary (2010) Estimating the kernel parameters of premises-based stochastic models of farmed animal infectious disease epidemics using limited, incomplete, or ongoing data. Theoretical Population Biology, Vol.78 (No.1). pp. 46-53. doi:10.1016/j.tpb.2010.04.003
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Official URL: http://dx.doi.org/10.1016/j.tpb.2010.04.003
Abstract
Three different estimators are presented for the types of parameters present in mathematical models of animal epidemics. The estimators make use of the data collected during an epidemic, which may be limited, incomplete, or under collection on an ongoing basis. When data are being collected on an ongoing basis, the estimated parameters can be used to evaluate putative control strategies. These estimators were tested using simulated epidemics based on a spatial, discrete-time, gravity-type, stochastic mathematical model containing two parameters. Target epidemics were simulated with the model and the three estimators were implemented using various combinations of collected data to independently determine the two parameters. (C) 2010 Elsevier Inc. All rights reserved.
| Item Type: | Journal Article | ||||
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| Subjects: | Q Science > QH Natural history > QH301 Biology Q Science > QH Natural history > QH426 Genetics |
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| Divisions: | Faculty of Science > Mathematics | ||||
| Journal or Publication Title: | Theoretical Population Biology | ||||
| Publisher: | Academic Press | ||||
| ISSN: | 0040-5809 | ||||
| Official Date: | August 2010 | ||||
| Dates: |
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| Volume: | Vol.78 | ||||
| Number: | No.1 | ||||
| Number of Pages: | 8 | ||||
| Page Range: | pp. 46-53 | ||||
| DOI: | 10.1016/j.tpb.2010.04.003 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Access rights to Published version: | Restricted or Subscription Access | ||||
| Funder: | National Institute of General Medical Sciences | ||||
| Grant number: | 5U01GM-076426 |
Data sourced from Thomson Reuters' Web of Knowledge
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