Parameter inference in small world network disease models with approximate Bayesian Computational methods
نویسندگان
چکیده
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 thesemodels have previously been realized using informed ‘‘guesses’’ of the proposedmodel parameters and tested for consistencywith 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. © 2009 Elsevier B.V. All rights reserved.
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