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

@inproceedings{Walker2009ParameterII,
  title={Parameter inference in small world network disease models with approximate Bayesian Computational methods},
  author={David M. Walker and David Allingham and H. W. Joseph Lee and Michael Small},
  year={2009}
}
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… CONTINUE READING