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

David M. Walker, David Allingham, Heung Wing Joseph Lee, Michael Small

Research output: Journal article publicationJournal articleAcademic researchpeer-review

17 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)540-548
Number of pages9
JournalPhysica A: Statistical Mechanics and its Applications
Issue number3
Publication statusPublished - 1 Feb 2010


  • Approximate Bayesian Computation
  • Epidemiological models
  • Small world networks
  • Stochastic simulation & inference

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Statistics and Probability

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