Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
976245 | Physica A: Statistical Mechanics and its Applications | 2010 | 9 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Mathematical Physics
Authors
David M. Walker, David Allingham, Heung Wing Joseph Lee, Michael Small,