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

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dc.contributor.authorCançado, André Luiz Fernandes
dc.contributor.authorDuarte, Anderson Ribeiro
dc.contributor.authorDuczmal, Luiz Henrique
dc.contributor.authorFerreira, Sabino José
dc.contributor.authorFonseca, Carlos M.
dc.contributor.authorGontijo, Eliane Dias
dc.date.accessioned2012-10-24T17:50:09Z
dc.date.available2012-10-24T17:50:09Z
dc.date.issued2010
dc.description.abstractBackground: Irregularly shape d 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 candid ate 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 geographicall y meaning ful 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 use d. In this pa per 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 algorithm s, 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 detect ion of moderately irregularly shaped clusters. The multi-objective cohesion scan is most effective for the detection of highly irregularly shaped clusters .pt_BR
dc.identifier.citationCANÇADO, A. L. F. et al. Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters. International Journal of Health Geographics, v.9, n. 55, p. 1-17, 2010. Disponível em: http://www.ij-healthgeographics.com/content/pdf/1476-072X-9-55.pdf. Acesso em: 24/10/2012pt_BR
dc.identifier.issn1476072X
dc.identifier.urihttp://www.repositorio.ufop.br/handle/123456789/1738
dc.language.isoen_USpt_BR
dc.rights.licenseAutores de artigos publicados no International Journal of Health Geographics são os detentores do copyright de seus artigos e concederam a qualquer terceiro o direito de usar, repoduzir ou disseminar o artigo. Fonte: International Journal of Health Geographics <http://www.ij-healthgeographics.com/about> Acesso em 01 Dez. 2013.
dc.titlePenalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters.pt_BR
dc.typeArtigo publicado em periodicopt_BR
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