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Prospective surveillance of multivariate spatial disease data

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Prospective surveillance of multivariate spatial disease data

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dc.contributor.author Corberán-Vallet, Ana es_ES
dc.date.accessioned 2015-06-19T10:17:57Z
dc.date.available 2015-06-19T10:17:57Z
dc.date.issued 2012 es_ES
dc.identifier.citation Statistical methods in medical research Vol. 21 Issue 5: pp. 457-477 es_ES
dc.identifier.uri http://hdl.handle.net/10550/44527
dc.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 article, 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. es_ES
dc.subject Disease surveillance es_ES
dc.subject multiple diseases es_ES
dc.subject shared component model es_ES
dc.subject conditional predictive ordinate es_ES
dc.title Prospective surveillance of multivariate spatial disease data es_ES
dc.type journal article es_ES
dc.identifier.doi 10.1177/0962280212446319 es_ES
dc.identifier.idgrec 072344 es_ES

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