Résumé
In geographical epidemiology, outcomes measured at the same spatial location may be correlated, so that the spatial structures of such outcomes across the region under consideration are very similar, perhaps because they reflect the same set of spatially distributed unobserved or unmeasured risk factors. Alternately, one outcome might lead to the presence of another over a region. Most studies fail to account for correlation among multiple outcomes. We demonstrate how a joint outcome modeling approach can improve the predictive accuracy of disease incidence over space in an infectious disease application in the forestry context.
Langue d'origine | English |
---|---|
Titre de la publication principale | Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases |
Maison d'édition | Wiley-Blackwell |
Pages | 283-296 |
Nombre de pages | 14 |
ISBN (électronique) | 9781118630013 |
ISBN (imprimé) | 9781118629932 |
DOI | |
Statut de publication | Published - janv. 30 2015 |
Publié à l'externe | Oui |
Note bibliographique
Publisher Copyright:© 2015 John Wiley & Sons, Inc. All rights reserved.
ASJC Scopus Subject Areas
- General Medicine
Empreinte numérique
Plonger dans les sujets de recherche 'Spatial Pattern Analysis of Multivariate Disease Data'. Ensemble, ils forment une empreinte numérique unique.Citer
Feng, C. X., & Dean, C. B. (2015). Spatial Pattern Analysis of Multivariate Disease Data. Dans Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases (pp. 283-296). Wiley-Blackwell. https://doi.org/10.1002/9781118630013.ch15