Parameter inference in small world network disease models with approximate Bayesian Computational methods

Title: Parameter inference in small world network disease models with approximate Bayesian Computational methods
Creator: Walker, David M.; Allingham, David; Lee, Heung Wing Joseph; Small, Michael
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
Publisher: Elsevier
Resource Type: journal article
Date: 2010
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.
Subject: small world networks; Approximate Bayesian Computation; stochastic simulation & inference; epidemiological models
Identifier: http://hdl.handle.net/1959.13/930626
Identifier: uon:10883
Identifier: ISSN:0378-4371
Language: eng
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