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@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 = "01 dez. 2020"
}


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