Spatial Pattern Analysis of Multivariate Disease Data

Cindy X. Feng, Charmaine B. Dean

Résultat de recherche: Chapter

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'origineEnglish
Titre de la publication principaleAnalyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases
Maison d'éditionWiley-Blackwell
Pages283-296
Nombre de pages14
ISBN (électronique)9781118630013
ISBN (imprimé)9781118629932
DOI
Statut de publicationPublished - janv. 30 2015
Publié à l'externeOui

Note bibliographique

Publisher Copyright:
© 2015 John Wiley & Sons, Inc. All rights reserved.

ASJC Scopus Subject Areas

  • General Medicine

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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