Fast detection of arbitrarily shaped disease clusters

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorAssunção, Renato Martins-
Autor(es): dc.creatorCosta, Marcelo Azevedo-
Autor(es): dc.creatorTavares, Andréa Iabrudi-
Autor(es): dc.creatorFerreira, S.-
Data de aceite: dc.date.accessioned2019-11-06T13:25:32Z-
Data de disponibilização: dc.date.available2019-11-06T13:25:32Z-
Data de envio: dc.date.issued2012-11-12-
Data de envio: dc.date.issued2012-11-12-
Data de envio: dc.date.issued2006-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/123456789/1766-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/555097-
Descrição: dc.descriptionDisease cluster detection and evaluation have commonly used spatial statistics methods that scan the map with a fixed circular window to locate candidate clusters. Recently, there has been interest in searching for clusters with arbitrary shape. The circular scan test retains high power of detecting a cluster, but does not necessarily identify the exact regions contained in a non-circular cluster particularly well. We propose, implement and evaluate a new procedure that is fast and produces clusters estimates of arbitrary shape in a rich class of possible cluster candidates. We showed that our methods contain the so-called upper level set method as a particular case. We present a power study of our method and, among other results, the main conclusion is that the likelihood-based arbitrarily shaped scan method is not appropriate to _nd a cluster estimate. When the parameter space includes the set of all possible spatial clusters in a map, a large and discrete parameter space, maximum likely cluster estimates tend to overestimate the true cluster by a large extent. This calls for a new approach different from the maximum likelihood method for this important public health problem.-
Idioma: dc.languageen-
Palavras-chave: dc.subjectDisease clusters-
Palavras-chave: dc.subjectScan statistics-
Palavras-chave: dc.subjectSpatial Cluster-
Palavras-chave: dc.subjectSpatial Statistics-
Título: dc.titleFast detection of arbitrarily shaped disease clusters-
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