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Sustainable Development Goals
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SCHISTOSOMIASIS RISK MAPPING IN THE STATE OF MINAS GERAIS, BRAZIL, USING A DECISION TREE APPROACH, REMOTE SENSING DATA AND SOCIOLOGICAL INDICATORS
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Instituto Nacional de Pesquisas Espaciais. São José dos Campos, SP, Brasil.
Instituto Nacional de Pesquisas Espaciais. São José dos Campos, SP, Brasil.
Instituto Nacional de Pesquisas Espaciais. São José dos Campos, SP, Brasil.
Fundação Oswaldo Cruz. Centro de Pesquisa René Rachou. Belo Horizonte, MG, Brasil / Santa Casa de Misericórdia. Programa de Pós-Graduação em Clínica Médica e Biomedicina. Belo Horizonte, MG, Brasil.
Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasília, DF, Brasil.
Secretaria de Estado de Saúde. Belo Horizonte, MG, Brasil.
Fundação Oswaldo Cruz. Centro de Pesquisa René Rachou. Belo Horizonte, MG, Brasil.
Instituto Nacional de Pesquisas Espaciais. São José dos Campos, SP, Brasil.
Instituto Nacional de Pesquisas Espaciais. São José dos Campos, SP, Brasil.
Fundação Oswaldo Cruz. Centro de Pesquisa René Rachou. Belo Horizonte, MG, Brasil / Santa Casa de Misericórdia. Programa de Pós-Graduação em Clínica Médica e Biomedicina. Belo Horizonte, MG, Brasil.
Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasília, DF, Brasil.
Secretaria de Estado de Saúde. Belo Horizonte, MG, Brasil.
Fundação Oswaldo Cruz. Centro de Pesquisa René Rachou. Belo Horizonte, MG, Brasil.
Abstract
Schistosomiasis mansoni is not just a physical disease, but is related to social and behavioural factors as well. Snails of the Biomphalaria genus are an intermediate host for Schistosoma mansoni and infect humans through water. The objective of this study is to classify the risk of schistosomiasis in the state of Minas Gerais (MG). We focus on socio¬economic and demographic features, basic sanitation features, the presence of accumulated water bodies, dense veg¬etation in the summer and winter seasons and related terrain characteristics. We draw on the decision tree approach to infection risk modelling and mapping. The model robustness was properly verified. The main variables that were selected by the procedure included the terrain’s water accumulation capacity, temperature extremes and the Human Development Index. In addition, the model was used to generate two maps, one that included risk classification for the entire of MG and another that included classification errors. The resulting map was 62.9% accurate.
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