RT conferenceObject
T1 Highly structured spatial models as a tool for analyzing the spread of diseases and species distributions
A1 Martínez-Minaya, Joaquín
A1 Vicent, Antonio
A1 López-Quílez, Antonio
A1 Picó, F.X.
A1 Marcer, A.
A1 Conesa, David
AB In the last years, the use of complex statistical models has increased to improve ourknowledge on the spread of diseases and the distribution of species, being of great interestin Ecology and Epidemiology. Complexity in these models arises for instancewhen including the use of beta likelihoods and spatial e ects. This complexity makesthe inferential and predictive processes challenging to perform. Bayesian statisticsrepresent a good alternative to deal with these models, because it is based on theidea that the information and uncertainty can be expressed in terms of probabilitydistributions. Moreover, this complexity can be readily handled with hierarchicalBayesian models without much di culty. However, despite the di erent advantagesof the Bayesian inference, the main challenge is to  nd an analytic expression forposterior distributions of the parameters and hyperparameters. Several numeric approacheshave been proposed such as Markov chain Monte Carlo methods (MCMC)or integrated nested Laplace approximation (INLA). Here, we present three di erentcomplex real problems which can be approached with hierarchical Bayesian modelsusing INLA. In particular, a beta regression model with random e ects to study apersimmon disease caused by the fungus Mycosphaerella nawae in the ComunitatValenciana region in Spain, and a beta spatial regression to study the spatial distributionof the genetic diversity of the plant Arabidopsis thaliana in the IberianPeninsula. In addition, we show a preliminary analysis of an emerging plant disease,known as the olive quick decline syndrome and caused by the bacterium Xylella fastidiosa,which is expanding rapidly in the southern region of Apulia in Italy, Corsica,continental France, as well as outbreaks in Balearic Islands in Spain.
YR 2017
FD 2017
LK http://hdl.handle.net/20.500.11939/5822
UL http://hdl.handle.net/20.500.11939/5822
LA en
NO Martínez-Minaya, J., Vicent, A., López-Quílez, A., Picó, F.X., Marcer, A., Conesa, D. (2017). Highly structured spatial models as a tool for analyzing the spread of diseases and species distributions. In 27th Annual Conference of the International Environmetrics Society, Bergamo, Italy.
DS MINDS@UW
RD Dec 4, 2023