Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/68670
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Type: Journal article
Title: Efficient methods for studying stochastic disease and population dynamics
Author: Keeling, M.
Ross, J.
Citation: Theoretical Population Biology, 2009; 75(2-3):133-141
Publisher: Academic Press Inc
Issue Date: 2009
ISSN: 0040-5809
1096-0325
Statement of
Responsibility: 
M.J. Keeling, J.V. Ross
Abstract: Stochastic ecological and epidemiological models are now routinely used to inform management and decision making throughout conservation and public-health. A difficulty with the use of such models is the need to resort to simulation methods when the population size (and hence the size of the state space) becomes large, resulting in the need for a large amount of computation to achieve statistical confidence in results. Here we present two methods that allow evaluation of all quantities associated with one- (and higher) dimensional Markov processes with large state spaces. We illustrate these methods using SIS disease dynamics and studying species that are affected by catastrophic events. The methods allow the possibility of extending exact Markov methods to real-world problems, providing techniques for efficient parameterisation and subsequent analysis.
Keywords: Markov processes; State space reduction; Disease dynamics; Catastrophes; Parameter estimation
Rights: Copyright © 2009 Elsevier Inc. All rights reserved.
RMID: 0020109204
DOI: 10.1016/j.tpb.2009.01.003
Appears in Collections:Mathematical Sciences publications

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