Highly structured spatial models as a tool for analyzing the spread of diseases and species distributions
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 Ecology and Epidemiology. Complexity in these models arises for instance when including the use of beta likelihoods...
| Autores principales: | , , , , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2018
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| Acceso en línea: | http://hdl.handle.net/20.500.11939/5822 https://graspa.org/wp-content/uploads/2017/07/TIES-GRASPA2017_BOA.pdf#page=126 |
| _version_ | 1855491990093824000 |
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| author | Martínez-Minaya, Joaquín Vicent, Antonio López-Quílez, Antonio Picó, F.X. Marcer, A. Conesa, David |
| author_browse | Conesa, David López-Quílez, Antonio Marcer, A. Martínez-Minaya, Joaquín Picó, F.X. Vicent, Antonio |
| author_facet | Martínez-Minaya, Joaquín Vicent, Antonio López-Quílez, Antonio Picó, F.X. Marcer, A. Conesa, David |
| author_sort | Martínez-Minaya, Joaquín |
| collection | ReDivia |
| description | 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 Ecology and Epidemiology. Complexity in these models arises for instance
when including the use of beta likelihoods and spatial e ects. This complexity makes
the inferential and predictive processes challenging to perform. Bayesian statistics
represent a good alternative to deal with these models, because it is based on the
idea that the information and uncertainty can be expressed in terms of probability
distributions. Moreover, this complexity can be readily handled with hierarchical
Bayesian models without much di culty. However, despite the di erent advantages
of the Bayesian inference, the main challenge is to nd 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)
or integrated nested Laplace approximation (INLA). Here, we present three di erent
complex real problems which can be approached with hierarchical Bayesian models
using INLA. In particular, a beta regression model with random e ects to study a
persimmon disease caused by the fungus Mycosphaerella nawae in the Comunitat
Valenciana region in Spain, and a beta spatial regression to study the spatial distribution
of the genetic diversity of the plant Arabidopsis thaliana in the Iberian
Peninsula. 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. |
| format | Objeto de conferencia |
| id | ReDivia5822 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| record_format | dspace |
| spelling | ReDivia58222025-04-25T14:51:16Z Highly structured spatial models as a tool for analyzing the spread of diseases and species distributions Martínez-Minaya, Joaquín Vicent, Antonio López-Quílez, Antonio Picó, F.X. Marcer, A. 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 Ecology and Epidemiology. Complexity in these models arises for instance when including the use of beta likelihoods and spatial e ects. This complexity makes the inferential and predictive processes challenging to perform. Bayesian statistics represent a good alternative to deal with these models, because it is based on the idea that the information and uncertainty can be expressed in terms of probability distributions. Moreover, this complexity can be readily handled with hierarchical Bayesian models without much di culty. However, despite the di erent advantages of the Bayesian inference, the main challenge is to nd 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) or integrated nested Laplace approximation (INLA). Here, we present three di erent complex real problems which can be approached with hierarchical Bayesian models using INLA. In particular, a beta regression model with random e ects to study a persimmon disease caused by the fungus Mycosphaerella nawae in the Comunitat Valenciana region in Spain, and a beta spatial regression to study the spatial distribution of the genetic diversity of the plant Arabidopsis thaliana in the Iberian Peninsula. 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. 2018-05-05T17:21:24Z 2018-05-05T17:21:24Z 2017 conferenceObject 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. http://hdl.handle.net/20.500.11939/5822 https://graspa.org/wp-content/uploads/2017/07/TIES-GRASPA2017_BOA.pdf#page=126 en 2017 27th Annual Conference of the International Environmetrics Society Bergamo, Italy Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ electronico |
| spellingShingle | Martínez-Minaya, Joaquín Vicent, Antonio López-Quílez, Antonio Picó, F.X. Marcer, A. Conesa, David Highly structured spatial models as a tool for analyzing the spread of diseases and species distributions |
| title | Highly structured spatial models as a tool for analyzing the spread of diseases and species distributions |
| title_full | Highly structured spatial models as a tool for analyzing the spread of diseases and species distributions |
| title_fullStr | Highly structured spatial models as a tool for analyzing the spread of diseases and species distributions |
| title_full_unstemmed | Highly structured spatial models as a tool for analyzing the spread of diseases and species distributions |
| title_short | Highly structured spatial models as a tool for analyzing the spread of diseases and species distributions |
| title_sort | highly structured spatial models as a tool for analyzing the spread of diseases and species distributions |
| url | http://hdl.handle.net/20.500.11939/5822 https://graspa.org/wp-content/uploads/2017/07/TIES-GRASPA2017_BOA.pdf#page=126 |
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