2009
DOI: 10.1016/j.actatropica.2008.10.012
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Spatial distribution of Biomphalaria mollusks at São Francisco River Basin, Minas Gerais, Brazil, using geostatistical procedures

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Cited by 32 publications
(27 citation statements)
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“…To overcome this problem and the limitation of the maximum number of data entries of the Spatial Planning for Regions in Growing Economies software (Câmara et al 1996), the mollusk at-tributes (class of species and localisation) were distributed along the drainage network of 15 river basins, according to the methodology used by Guimarães et al (2009 Indicator kriging may be defined as a technique of statistical inference, which allows the estimation of values and the uncertainties associated with the attribute during the spatialisation of a sample property (Felgueiras 1999). It is a nonlinear estimator, which is applied on a sample set of the attribute whose values are modified according to a nonlinear transformation.…”
Section: Schistosomiasismentioning
confidence: 99%
“…To overcome this problem and the limitation of the maximum number of data entries of the Spatial Planning for Regions in Growing Economies software (Câmara et al 1996), the mollusk at-tributes (class of species and localisation) were distributed along the drainage network of 15 river basins, according to the methodology used by Guimarães et al (2009 Indicator kriging may be defined as a technique of statistical inference, which allows the estimation of values and the uncertainties associated with the attribute during the spatialisation of a sample property (Felgueiras 1999). It is a nonlinear estimator, which is applied on a sample set of the attribute whose values are modified according to a nonlinear transformation.…”
Section: Schistosomiasismentioning
confidence: 99%
“…Different spatial modelling techniques have been used to model Biomphalaria distribution such as maximum entropy (MaxEnt) (Scholte et al, 2012;Stensgaard et al, 2013;Pedersen et al, 2014), genetic algorithm for rule-set prediction (GARP) (Stensgaard et al, 2006), and geostatic indicator Kriging (Guimarães et al, 2009). There are also non-regression models such as the Bayesian geostatistical approach for modelling Biomphalaria spp., distribution (Raso et al, 2005;Vounatsou et al, 2009;Standley et al, 2012;Schur et al, 2013).…”
Section: Introductionmentioning
confidence: 99%
“…The conditions for the positive localities were that B. glabrata only, B. straminea only or B. glabrata + B. straminea were present, while areas where there were no snails were considered negative. For the municipalities for which there was no information regarding the geolocation of Biomphalaria, the attributes (presence or absence of snails) were distributed across the six hydrographic basins of Pernambuco that were present within the study area, in accordance with the methodology suggested by Guimarães et al (2009). This methodology consists of distribute a point sequence along the drainage network when there isn't a geolocation of breeding places.…”
Section: Krigingmentioning
confidence: 99%
“…In particular, these techniques have been used to model diseases such as schistosomiasis in studies developed by Guimarães et al (2009Guimarães et al ( , 2010Guimarães et al ( , 2012 to estimate the spatial distribution of Biomphalaria snails in the state of Minas Gerais. Furthermore, Scholte et al (2012) used modelling based on environmental characteristics with this aim in the whole country.…”
Section: Geospatial Healthmentioning
confidence: 99%