Analysis and estimative of schistosomiasis prevalence for the state of Minas Gerais, Brazil, using multiple regression with social and environmental spatial data

dc.creatorGuimarães, Ricardo José de Paula Souza
dc.creatorFreitas, Corina da Costa
dc.creatorDutra, Luciano Vieira
dc.creatorMoura, Ana Clara Mourão
dc.creatorAmaral, Ronaldo Santos do
dc.creatorDrummond, Sandra Costa
dc.creatorGuerra, Márcio
dc.creatorScholte, Ronaldo Guilherme Carvalho
dc.creatorFreitas, Charles R.
dc.creatorCarvalho, Omar dos Santos
dc.date2013-06-05T14:12:16Z
dc.date2013-06-05T14:12:16Z
dc.date2006
dc.date.accessioned2023-09-26T23:41:24Z
dc.date.available2023-09-26T23:41:24Z
dc.identifierGUIMARÃES, Ricardo José de Paula Souza et al. Analysis and estimative of schistosomiasis prevalence for the state of Minas Gerais, Brazil, using multiple regression with social and environmental spatial data. Memórias do Instituto Oswaldo Cruz, Rio de Janeiro, v. 101, Suppl. I, p. 91-96, 2006.
dc.identifier0074-0276
dc.identifierhttps://www.arca.fiocruz.br/handle/icict/6560
dc.identifier10.1590/S0074-02762006000900014.
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8893202
dc.descriptionCNPq (309922/2003-8; 305546/2003-1; 380203/2004-9) Fapemig (EDT 1775/03; EDT 61775/03; CRA 0070/04)
dc.descriptionThe aim of this work is to establish a relationship between schistosomiasis prevalence and social-environmental variables, in the state of Minas Gerais, Brazil, through multiple linear regression. The final regression model was established, after a variables selection phase, with a set of spatial variables which contains the summer minimum temperature, human development index, and vegetation type variables. Based on this model, a schistosomiasis risk map was built for Minas Gerais.
dc.formatapplication/pdf
dc.languageeng
dc.rightsopen access
dc.subjectSchistosomiasis/risk map
dc.subjectGeographical information system
dc.subjectMultiple linear regression
dc.titleAnalysis and estimative of schistosomiasis prevalence for the state of Minas Gerais, Brazil, using multiple regression with social and environmental spatial data
dc.typeArticle


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