Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25478
Title: Parameter inference in small world network disease models with approximate Bayesian Computational methods
Authors: Walker, DM
Allingham, D
Lee, HWJ 
Small, M
Keywords: Approximate Bayesian Computation
Epidemiological models
Small world networks
Stochastic simulation & inference
Issue Date: 2010
Publisher: North-Holland
Source: Physica A. Statistical mechanics and its applications, 2010, v. 389, no. 3, p. 540-548 How to cite?
Journal: Physica A. Statistical mechanics and its applications 
Abstract: 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.
URI: http://hdl.handle.net/10397/25478
ISSN: 0378-4371
EISSN: 1873-2119
DOI: 10.1016/j.physa.2009.09.053
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