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

[1]  C. K. Michael Tse,et al.  Small World and Scale Free Model of Transmission of SARS , 2005, Int. J. Bifurc. Chaos.

[2]  Brian Tomlinson,et al.  SARS: experience at Prince of Wales Hospital, Hong Kong , 2003, The Lancet.

[3]  Yoshiharu Maeno,et al.  Profiling of a network behind an infectious disease outbreak , 2009, ArXiv.

[4]  L. Allen.  An introduction to stochastic processes with applications to biology , 2003 .

[5]  Robert King,et al.  Further DNA segmentation analysis using approximate Bayesian computation , 2008 .

[6]  N. Rashevsky,et al.  Mathematical biology , 1961, Connecticut medicine.

[7]  Paul Marjoram,et al.  Markov chain Monte Carlo without likelihoods , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Yoshiharu Maeno,et al.  Discovery of a missing disease spreader , 2010, Physica A: Statistical Mechanics and its Applications.

[9]  M. Small,et al.  Super-spreaders and the rate of transmission of the SARS virus , 2006, Physica D: Nonlinear Phenomena.

[10]  S. Strogatz.  Exploring complex networks , 2001, Nature.

[11]  C. Fraser,et al.  Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong , 2003, The Lancet.

[12]  Mark M. Tanaka,et al.  Sequential Monte Carlo without likelihoods , 2007, Proceedings of the National Academy of Sciences.

[13]  S. E. Ahmed,et al.  Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference , 2008, Technometrics.

[14]  A N Pettitt,et al.  Bayesian modelling of an epidemic of severe acute respiratory syndrome , 2006, Bulletin of mathematical biology.

[15]  A. Abbott,et al.  Biologists seek to head off future sources of infection , 2003, Nature.