6533b854fe1ef96bd12af047
RESEARCH PRODUCT
Bayesian correlated models for assessing the prevalence of viruses in organic and non-organic agroecosystems
E. LázaroCarmen ArmeroL. Rubiosubject
Hellinger distancesensitivity analysisHellinger distance model robustness risk infection sensitivity analysis virus epidemiology:Matemàtiques i estadística::Estadística matemàtica [Àrees temàtiques de la UPC]:62 Statistics::62F Parametric inference [Classificació AMS]:62 Statistics::62J Linear inference regression [Classificació AMS]model robustnessvirus epidemiology:62 Statistics::62P Applications [Classificació AMS]62-07 62F15 62J12 62P10 62P12risk infectiondescription
Cultivation of horticultural species under organic management has increased in importance in recent years. However, the sustainability of this new production method needs to be supported by scientific research, especially in the field of virology. We studied the prevalence of three important virus diseases in agroecosystems with regard to its management system: organic versus non-organic, with and without greenhouse. Prevalence was assessed by means of a Bayesian correlated binary model which connects the risk of infection of each virus within the same plot and was defined in terms of a logit generalized linear mixed model (GLMM). Model robustness was checked through a sensitivity analysis based on different hyperprior scenarios. Inferential results were examined in terms of changes in the marginal posterior distributions, both for fixed and for random effects, through the Hellinger distance and a derived measure of sensitivity. Statistical results suggested that organic systems show lower or similar prevalence than non-organic ones in both single and multiple infections as well as the relevance of the prior specification of the random effects in the inferential process.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-06-21 | SORT- Statistics and Operations Research Transactions |