Parameter inference in small world network disease models with approximate Bayesian Computational methods
AbstractSmall 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.
Volume (Year): 389 (2010)
Issue (Month): 3 ()
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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/
Small world networks; Approximate Bayesian Computation; Stochastic simulation & inference; Epidemiological models;
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- Maeno, Yoshiharu, 2010. "Discovering network behind infectious disease outbreak," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4755-4768.
- Maeno, Yoshiharu, 2011. "Discovery of a missing disease spreader," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3412-3426.
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