Bayesian estimation for percolation models of disease spread in plant populations

G. J. Gibson, W. Otten, J. A N Filipe, A. Cook, G. Marion, C. A. Gilligan

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


Statistical methods are formulated for fitting and testing percolation-based, spatio-temporal models that are generally applicable to biological or physical processes that evolve in spatially distributed populations. The approach is developed and illustrated in the context of the spread of Rhizoctonia solani, a fungal pathogen, in radish but is readily generalized to other scenarios. The particular model considered represents processes of primary and secondary infection between nearest-neighbour hosts in a lattice, and time-varying susceptibility of the hosts. Bayesian methods for fitting the model to observations of disease spread through space and time in replicate populations are developed. These use Markov chain Monte Carlo methods to overcome the problems associated with partial observation of the process. We also consider how model testing can be achieved by embedding classical methods within the Bayesian analysis. In particular we show how a residual process, with known sampling distribution, can be defined. Model fit is then examined by generating samples from the posterior distribution of the residual process, to which a classical test for consistency with the known distribution is applied, enabling the posterior distribution of the P-value of the test used to be estimated. For the Rhizoctonia-radish system the methods confirm the findings of earlier non-spatial analyses regarding the dynamics of disease transmission and yield new evidence of environmental heterogeneity in the replicate experiments. © Springer Science + Business Media, LLC 2006.

Original languageEnglish
Pages (from-to)391-402
Number of pages12
JournalStatistics and Computing
Issue number4
Publication statusPublished - Dec 2006


  • Bayesian inference
  • Fungal pathogens
  • Markov chain Monte Carlo
  • Spatio-temporal modeling
  • Stochastic modelling


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