Rating
Title	Prospective surveillance of multivariate spatial disease data
Author	Corberan-Vallet, Ana; Lawson, Andrew B.
Date	2011
Subject.MeSH	Disease surveillance Conditional predictive ordinate Shared component model Multiple diseases
Description	Abstract: Surveillance systems are often focused on more than one disease within a predefined area. On those occasions when outbreaks of disease are likely to be correlated, the use of multivariate surveillance techniques integrating information from multiple diseases allows us to improve the sensitivity and timeliness of outbreak detection. In this paper, we present an extension of the surveillance conditional predictive ordinate to monitor multivariate spatial disease data. The proposed surveillance technique, which is defined for each small area and time period as the conditional predictive distribution of those counts of disease higher than expected given the data observed up to the previous time period, alerts us to both small areas of increased disease incidence and the diseases causing the alarm within each area. We investigate its performance within the framework of Bayesian hierarchical Poisson models using a simulation study. An application to diseases of the respiratory system in South Carolina is finally presented. Keywords: disease surveillance; multiple diseases; Shared component model; conditional predictive ordinate
Collection	MUSC Department of Public Health Sciences Working Papers Working Papers
Contributing Institution	Waring Historical Library (MUSC)
Date Digital	2011
Rights	Copyright is held by the author.
Language	English
Type	Text
Format	application/pdf
Media Type	Manuscripts
Resource Identifier	11-006
Bibliographic Citation	Corberan-Vallet, Ana; Lawson, Andrew B. Prospective surveillance of multivariate spatial disease data, MUSC Department of Public Health Sciences Working Papers, 2011. http://medica.library.musc.edu/p/bew/107

Prospective surveillance of multivariate spatial disease data

Description

Tags

Comments