Keeling, Matthew James and Ross, Joshua V.. (2009) Efficient methods for studying stochastic disease and population dynamics. Theoretical Population Biology, Vol.75 (No.2-3). pp. 133-141. ISSN 0040-5809

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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. (C) 2009 Elsevier Inc. All rights reserved

Item Type:	Journal Article
Subjects:	Q Science > QH Natural history > QH301 Biology Q Science > QH Natural history > QH426 Genetics
Divisions:	Faculty of Science > Life Sciences (2010- ) > Biological Sciences ( -2010) Faculty of Science > Mathematics
Journal or Publication Title:	Theoretical Population Biology
Publisher:	Academic Press
ISSN:	0040-5809
Date:	March 2009
Volume:	Vol.75
Number:	No.2-3
Number of Pages:	9
Page Range:	pp. 133-141
Identification Number:	10.1016/j.tpb.2009.01.003
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
URI:	http://wrap.warwick.ac.uk/id/eprint/28039

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