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Conditional predictive inference for online surveillance of spatial disease incidence

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Conditional predictive inference for online surveillance of spatial disease incidence

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dc.contributor.author Corberán-Vallet, Ana es_ES
dc.contributor.author Lawson, Andrew B. es_ES
dc.date.accessioned 2015-06-22T09:50:07Z
dc.date.available 2015-06-22T09:50:07Z
dc.date.issued 2011 es_ES
dc.identifier.citation Statistics in medicine Vol. 30 Issue 26: pp. 3095-3116 es_ES
dc.identifier.uri http://hdl.handle.net/10550/44669
dc.description.abstract This paper deals with the development of statistical methodology for timely detection of incident disease clusters in space and time. The increasing availability of data on both the time and the location of events enables the construction of multivariate surveillance techniques, which may enhance the ability to detect localized clusters of disease relative to the surveillance of the overall count of disease cases across the entire study region. We introduce the surveillance conditional predictive ordinate as a general Bayesian model-based surveillance technique that allows us to detect small areas of increased disease incidence when spatial data are available. To address the problem of multiple comparisons, we incorporate a common probability that each small area signals an alarm when no change in the risk pattern of disease takes place into the analysis. We investigate the performance of the proposed surveillance technique within the framework of Bayesian hierarchical Poisson models using a simulation study. Finally, we present a case study of salmonellosis in South Carolina. es_ES
dc.subject public health surveillance es_ES
dc.subject spatial data es_ES
dc.subject Bayesian hierarchical models es_ES
dc.subject lagged loss function es_ES
dc.subject conditional predictive ordinate es_ES
dc.subject multiple comparisons es_ES
dc.title Conditional predictive inference for online surveillance of spatial disease incidence es_ES
dc.type journal article es_ES
dc.identifier.doi 10.1002/sim.4340 es_ES
dc.identifier.idgrec 072343 es_ES

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