Bayesian hierarchical modelling of the olive quick decline syndrome in south-eastern Italy
2017
Vicent, Antonio | Martínez-Minaya, Joaquín | López-Quílez, Antonio | Conesa, David
In the last years, the use of complex statistical models has increased to improve our knowledge on the spread of diseases and the distribution of species, being of great interest in plant disease epidemiology. The complexity of these models makes the inferential and predictive processes challenging to perform. Bayesian statistics represents a good alternative, because it is based on the premise that both information and uncertainty can be expressed in terms of probability distributions. Despite the advantages of Bayesian inference, the main challenge is to find an analytic expression for posterior distributions of the parameters and hyperparameters. Several numeric approaches have been proposed, such as Markov chain Monte Carlo methods (MCMC) and integrated nested Laplace approximation (INLA). Here, we present different spatio-temporal analyses using INLA for the geographical spread of the olive quick decline syndrome, a lethal plant disease caused by the bacterium Xylella fastidiosa in south-eastern Italy.
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