Author | Fonseca, Fernanda Rodrigues | |
Author | Freitas, Corina da Costa | |
Author | Dutra, Luciano Vieira | |
Author | Guimarães, Ricardo José de Paula Souza e | |
Author | Carvalho, Omar dos Santos | |
Access date | 2015-06-30T12:56:15Z | |
Available date | 2015-06-30T12:56:15Z | |
Document date | 2014 | |
Citation | FONSECA, Fernanda Rodrigues et al. Spatial modeling of the schistosomiasis mansoni in Minas GeraisState, Brazil using spatial regression. Acta Tropica, v. 133, p. 56-63, May 2014. | pt_BR |
ISSN | 0001-706X | |
URI | https://www.arca.fiocruz.br/handle/icict/11008 | |
Sponsorship | CNPq, CAPES, FAPESP and FAPEMIG | pt_BR |
Language | eng | pt_BR |
Publisher | Elsevier | pt_BR |
Rights | open access | pt_BR |
Title | Spatial modeling of the schistosomiasis mansoni in Minas Gerais State, Brazil using spatial regression | pt_BR |
Type | Article | pt_BR |
DOI | 10.1016/j.actatropica.2014.01.015 | |
Abstract | Schistosomiasis 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 estimate the prevalence of schistosomiasis in Minas Gerais State in Brazil using spatial disease information derived from the state transportation network of roads and rivers. The spatial information was incorporated in two ways: by introducing new variables that carry spatial neighborhood information and by using spatial regression models. Climate, socioeconomic and environmental variables were also used as co-variables to build models and use them to estimate a risk map for the whole state of Minas Gerais. The results show that the models constructed from the spatial regression produced a better fit, providing smaller root mean square error (RMSE) values. When no spatial information was used, the RMSE for the whole state of Minas Gerais reached 9.5%; with spatial regression, the RMSE reaches 8.8% (when the new variables are added to the 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 the spread of disease and identified certain conditions that are favorable for disease development. The use of spatial regression for the network of roads and rivers produced meaningful results for health management procedures and directing activities, enabling better detection of disease risk areas. | pt_BR |
Affilliation | Instituto Nacional de Pesquisas Espaciais. São José dos Campos, SP, Brazil / Fundação Oswaldo Cruz. Instituto Leônidas e Maria Deane. Manaus, AM, Brazil. | pt_BR |
Affilliation | Instituto Nacional de Pesquisas Espaciais. São José dos Campos, SP, Brazil. | pt_BR |
Affilliation | Instituto Nacional de Pesquisas Espaciais. São José dos Campos, SP, Brazil. | pt_BR |
Affilliation | Instituto Nacional de Pesquisas Espaciais. São José dos Campos, SP, Brazil / Instituto Evandro Chagas. Ananindeua, PA, Brazil. | pt_BR |
Affilliation | Fundação Oswaldo Cruz. Centro de Pesquisas René Rachou. Laboratório de Esquistossomose. Belo Horizonte, MG, Brazil. | pt_BR |
Subject | Spatial relations | pt_BR |
Subject | Generalized proximity matrices | pt_BR |
Subject | Spatial analysis | pt_BR |
Subject | Regression analysis | pt_BR |
Subject | Schistosomiasis mansonia | pt_BR |