Measuring the vulnerability of the Uruguayan population to vector-borne diseases via spatially hierarchical factor models
dc.contributor.author | HEDIBERT FREITAS LOPES | |
dc.contributor.author | Schmidt, Alexandra M. | |
dc.contributor.author | Salazar, Esther | |
dc.contributor.author | Gómez, Mariana | |
dc.contributor.author | Achkar, Marcel | |
dc.coverage.cidade | Não informado | pt_BR |
dc.coverage.pais | Não Informado | pt_BR |
dc.creator | Schmidt, Alexandra M. | |
dc.creator | Salazar, Esther | |
dc.creator | Gómez, Mariana | |
dc.creator | Achkar, Marcel | |
dc.date.accessioned | 2022-08-19T18:58:41Z | |
dc.date.available | 2022-08-19T18:58:41Z | |
dc.date.issued | 2012 | |
dc.description.other | We propose a model-based vulnerability index of the population from Uruguay to vector-borne diseases. We have available measurements of a set of variables in the census tract level of the 19 Departmental capitals of Uruguay. In particular, we propose an index that combines different sources of information via a set of micro-environmental indicators and geographical location in the country. Our index is based on a new class of spatially hierarchical factor models that explicitly account for the different levels of hierarchy in the country, such as census tracts within the city level, and cities in the country level. We compare our approach with that obtained when data are aggregated in the city level. We show that our proposal outperforms current and standard approaches, which fail to properly account for discrepancies in the region sizes, for example, number of census tracts. We also show that data aggregation can seriously affect the estimation of the cities vulnerability rankings under benchmark models. | pt_BR |
dc.format.extent | p. 284-303 | pt_BR |
dc.format.medium | Digital | pt_BR |
dc.identifier.doi | 10.1214/11-AOAS497 | pt_BR |
dc.identifier.issue | 1 | pt_BR |
dc.identifier.uri | https://repositorio.insper.edu.br/handle/11224/4057 | |
dc.identifier.volume | 6 | pt_BR |
dc.language.iso | Inglês | pt_BR |
dc.publisher | Institute of Mathematical Statistics | pt_BR |
dc.relation.ispartof | The Annals of Applied Statistics | pt_BR |
dc.rights.license | O INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR. | pt_BR |
dc.subject.keywords | Areal data | pt_BR |
dc.subject.keywords | Bayesian inference | pt_BR |
dc.subject.keywords | model comparison | pt_BR |
dc.subject.keywords | spatial interpolation | pt_BR |
dc.subject.keywords | spatial smoothing | pt_BR |
dc.title | Measuring the vulnerability of the Uruguayan population to vector-borne diseases via spatially hierarchical factor models | pt_BR |
dc.type | journal article | |
dspace.entity.type | Publication | |
local.identifier.sourceUri | https://projecteuclid.org/journals/annals-of-applied-statistics/volume-6/issue-1/Measuring-the-vulnerability-of-the-Uruguayan-population-to-vector-borne/10.1214/11-AOAS497.full?tab=ArticleLink | |
local.subject.cnpq | Ciências Sociais Aplicadas | pt_BR |
local.type | Artigo Científico | pt_BR |
relation.isAuthorOfPublication | 41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca | |
relation.isAuthorOfPublication.latestForDiscovery | 41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca |
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