@Article{FonsecaFreDutGuiCar:2014:SpMoSc,
author = "Fonseca, Fernanda Rodrigues and Freitas, Corina da Costa and
Dutra, Luciano Vieira and Guimar{\~a}es, Ricardo J. P. S. and
Carvalho, O.",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and Instituto Nacional de Pesquisas
Espaciais/INPE, Av. dos Astronautas, 1758 Jd. Granja, CEP
12227-010 S{\~a}o Jos{\'e} dos Campos, SP, Brazil; Instituto
Evandro Chagas/IEC, Rodovia BR-316 km 7 Levil{\^a}ndia, CEP
67030-000 Ananindeua, PA, Brazil and Centro de Pesquisas Ren{\'e}
Rachou/FIOCRUZ, Av. Augusto de Lima, 1715 Barro Preto, CEP
30190-002 Belo Horizonte, MG, Brazil",
title = "Spatial modeling of the schistosomiasis mansoni in Minas Gerais
State, Brazil using spatial regression",
journal = "Acta Tropica",
year = "2014",
volume = "133",
number = "1",
pages = "56--63",
keywords = "disease spread, health impact, health risk, neighborhood, public
health, regression analysis, schistosomiasis, spatial analysis,
taxonomy, article, Brazil, climate, disease course, disease
transmission, environmental factor, health care management, human,
human development, mathematical variable, medical information,
neighborhood, precipitation, prevalence, risk assessment, river,
sanitation, schistosomiasis mansoni, socioeconomics, spatial
modeling, spatial regression, statistical analysis, statistical
model, temperature, topography, traffic and transport, vegetation,
Brazil, Minas Gerais.",
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. © 2014 Elsevier B.V.",
doi = "10.1016/j.actatropica.2014.01.015",
url = "http://dx.doi.org/10.1016/j.actatropica.2014.01.015",
issn = "0001-706X and 1873-6254",
label = "scopus 2014-05 FonsecaFreDutGuiCar:2014:SpMoSc",
language = "en",
targetfile = "Fernanda_Acta_2104.pdf",
urlaccessdate = "03 maio 2024"
}