Spatial modeling of the schistosomiasis mansoni in Minas Gerais State, Brazil using spatial regression

dc.creatorFonseca, Fernanda Rodrigues
dc.creatorFreitas, Corina da Costa
dc.creatorDutra, Luciano Vieira
dc.creatorGuimarães, Ricardo José de Paula Souza e
dc.creatorCarvalho, Omar dos Santos
dc.date2015-06-30T12:56:15Z
dc.date2015-06-30T12:56:15Z
dc.date2014
dc.date.accessioned2023-09-26T22:27:15Z
dc.date.available2023-09-26T22:27:15Z
dc.identifierFONSECA, Fernanda Rodrigues et al. Spatial modeling of the schistosomiasis mansoni in Minas GeraisState, Brazil using spatial regression. Acta Tropica, vol. 133, p. 56-63, 2014.
dc.identifier0001-706X
dc.identifierhttps://www.arca.fiocruz.br/handle/icict/11008
dc.identifier10.1016/j.actatropica.2014.01.015
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8879426
dc.descriptionCNPq, CAPES, FAPESP and FAPEMIG
dc.descriptionSchistosomiasis is a transmissible parasitic disease caused by the etiologic agent Schistosoma mansoni,whose intermediate hosts are snails of the genus Biomphalaria. The main goal of this paper is to estimatethe prevalence of schistosomiasis in Minas Gerais State in Brazil using spatial disease information derivedfrom the state transportation network of roads and rivers. The spatial information was incorporated intwo ways: by introducing new variables that carry spatial neighborhood information and by using spatialregression models. Climate, socioeconomic and environmental variables were also used as co-variablesto build models and use them to estimate a risk map for the whole state of Minas Gerais. The results showthat the models constructed from the spatial regression produced a better fit, providing smaller root meansquare error (RMSE) values. When no spatial information was used, the RMSE for the whole state of MinasGerais reached 9.5%; with spatial regression, the RMSE reaches 8.8% (when the new variables are added tothe model) and 8.5% (with the use of spatial regression). Variables representing vegetation, temperature,precipitation, topography, sanitation and human development indexes were important in explaining thespread of disease and identified certain conditions that are favorable for disease development. The use ofspatial regression for the network of roads and rivers produced meaningful results for health managementprocedures and directing activities, enabling better detection of disease risk areas
dc.formatapplication/pdf
dc.languageeng
dc.publisherElsevier B.V.
dc.rightsopen access
dc.subjectSpatial relations
dc.subjectGeneralized proximity matrices
dc.subjectSpatial analysis
dc.subjectRegression analysis
dc.subjectSchistosomiasis mansonia
dc.titleSpatial modeling of the schistosomiasis mansoni in Minas Gerais State, Brazil using spatial regression
dc.typeArticle


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