Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters

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Autor(es): dc.creatorCançado, André Luiz Fernandes-
Autor(es): dc.creatorDuarte, Anderson Ribeiro-
Autor(es): dc.creatorDuczmal, Luiz Henrique-
Autor(es): dc.creatorFerreira Neto, Sabino José-
Autor(es): dc.creatorFonseca, Carlos M.-
Autor(es): dc.creatorGontijo, Eliane Dias-
Data de aceite: dc.date.accessioned2021-10-14T18:03:04Z-
Data de disponibilização: dc.date.available2021-10-14T18:03:04Z-
Data de envio: dc.date.issued2013-03-15-
Data de envio: dc.date.issued2013-03-15-
Data de envio: dc.date.issued2010-
Fonte completa do material: dc.identifierhttp://repositorio.unb.br/handle/10482/12491-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/622393-
Descrição: dc.descriptionBackground: Irregularly shaped spatial clusters are difficult to delineate. A cluster found by an algorithm often spreads through large portions of the map, impacting its geographical meaning. Penalized likelihood methods for Kulldorff’s spatial scan statistics have been used to control the excessive freedom of the shape of clusters. Penalty functions based on cluster geometry and non-connectivity have been proposed recently. Another approach involves the use of a multi-objective algorithm to maximize two objectives: the spatial scan statistics and the geometric penalty function. Results & Discussion: We present a novel scan statistic algorithm employing a function based on the graph topology to penalize the presence of under-populated disconnection nodes in candidate clusters, the disconnection nodes cohesion function. A disconnection node is defined as a region within a cluster, such that its removal disconnects the cluster. By applying this function, the most geographically meaningful clusters are sifted through the immense set of possible irregularly shaped candidate cluster solutions. To evaluate the statistical significance of solutions for multi-objective scans, a statistical approach based on the concept of attainment function is used. In this paper we compared different penalized likelihoods employing the geometric and non-connectivity regularity functions and the novel disconnection nodes cohesion function. We also build multi-objective scans using those three functions and compare them with the previous penalized likelihood scans. An application is presented using comprehensive state-wide data for Chagas’ disease in puerperal women in Minas Gerais state, Brazil. Conclusions: We show that, compared to the other single-objective algorithms, multi-objective scans present better performance, regarding power, sensitivity and positive predicted value. The multi-objective non-connectivity scan is faster and better suited for the detection of moderately irregularly shaped clusters. The multi-objective cohesion scan is most effective for the detection of highly irregularly shaped clusters.-
Formato: dc.formatapplication/pdf-
Publicador: dc.publisherBioMed Central-
Direitos: dc.rightsAcesso Aberto-
Direitos: dc.rights© 2010 Cançado et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Fonte: http://www.ij-healthgeographics.com/content/9/1/55. Acesso em: 15 mar. 2013-
Palavras-chave: dc.subjectAnálise espacial (Estatística)-
Palavras-chave: dc.subjectAnálise por conglomerados-
Palavras-chave: dc.subjectMapeamento temático-
Palavras-chave: dc.subjectMapeamento - doenças-
Título: dc.titlePenalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters-
Tipo de arquivo: dc.typelivro digital-
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