Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/930626

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Title
Parameter inference in small world network disease models with approximate Bayesian Computational methods
Author/Creator
Walker, David M.;  Allingham, David;  Lee, Heung Wing Joseph;  Small, Michael
Institution
The University of Newcastle. Faculty of Science & Information Technology, School of Mathematical and Physical Sciences
Description
Small world network models have been effective in capturing the variable behaviour of reported case data of the SARS coronavirus outbreak in Hong Kong during 2003. Simulations of these models have previously been realized using informed “guesses” of the proposed model parameters and tested for consistency with the reported data by surrogate analysis. In this paper we attempt to provide statistically rigorous parameter distributions using Approximate Bayesian Computation sampling methods. We find that such sampling schemes are a useful framework for fitting parameters of stochastic small world network models where simulation of the system is straightforward but expressing a likelihood is cumbersome.
Relation
Physica A: Statistical Mechanics and its Applications Vol. 389, Issue 3, p. 540-548
Publisher Link
http://dx.doi.org/10.1016/j.physa.2009.09.053
Date
2010
Publisher
Elsevier
Keyword(s)
small world networks;  Approximate Bayesian Computation;  stochastic simulation & inference;  epidemiological models
Resource Type
journal article
Identifier
http://hdl.handle.net/1959.13/930626
Identifier
ISSN:0378-4371
Reviewed
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Subject
"Physica A: Statistical Mechanics and its Applications"
 
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