Spatial distribution of Biomphalaria mollusks at São Francisco River Basin, Minas Gerais, Brazil, using geostatistical procedures

Guimarães, Ricardo José de Paula Souza e; Freitas, Corina da Costa; Dutra, Luciano Vieira; Felgueiras, Carlos; Moura, Ana Clara Mourão; Amaral, Ronaldo; Drummond, Sandra; Scholte, Ronaldo Guilherme Carvalho; Oliveira, Guilherme; Carvalho, Omar S.

doi:10.1016/j.actatropica.2008.10.012

Cited by 34 publications

(29 citation statements)

References 17 publications

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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%

A geoprocessing approach for studying and controlling schistosomiasis in the state of Minas Gerais, Brazil

Guimarães¹,

Freitas

Dutra

et al. 2010

Mem. Inst. Oswaldo Cruz

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Section: Schistosomiasismentioning

confidence: 99%

A geoprocessing approach for studying and controlling schistosomiasis in the state of Minas Gerais, Brazil

Guimarães¹,

Freitas

Dutra

et al. 2010

Mem. Inst. Oswaldo Cruz

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“…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%

Predicting the spatial distribution of Biomphalaria straminea, a potential intermediate host for Schistosoma mansoni, in China

Habib

Guo

et al. 2016

Geospat Health

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Schistosomiasis is one of the most prevalent parasitic diseases impacting human health in the tropics and sub-tropics. The geographic distribution of Schistosoma mansoni, the most widespread species, includes areas in Africa, the Middle East, South America and the Caribbean. Snails of the genus Biomphalaria act as intermediate host for S. mansoni. Biomphalaria straminea is not indigenous in China but was accidentally introduced to Hong Kong from South America and has spread to other habitats in the southern parts of the country. This species is known for its great dispersal capacity that highlights the importance of the snail as a potential host for S. mansoni in China. In this connection, although no such infection has been recorded in the field so far, the continuous expansion of China's projects in endemic areas of Africa and import of the infection via returning workers or visitors deserve attention. The purpose of this study was to map and predict the spatial distribution of B. straminea in China. Snail occurrence data were assembled and investigated using MaxEnt software, along with climatic and environmental variables to produce a predictive risk map. Of the environmental variables tested, the precipitation of warmest quarter was the most contribution factor for snail's spatial distribution. Risk areas were found in southeastern China and it is expected that they will guide policies and control programmes to potential areas area of snail abundance and used for spatial targeting of control interventions for this invasive species.

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“…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%

Modelling of the distribution of Biomphalaria glabrata and Biomphalaria straminea in the metropolitan region of Recife, Pernambuco, Brazil

Barbosa¹,

Guimarães²,

Loyo³

et al. 2016

Geospat Health

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The occurrence of schistosomiasis is directly linked to the presence of its snail intermediate host Biomphalaria spp. Knowledge of geographical distribution, habitats and behaviour of these snails in relation to the climate is essential for guiding measures for disease prevention and control. This study aims to model the distribution of B. glabrata and B. straminea in schistosomiasis non-endemic areas of the metropolitan region of Recife (MRR) based on environmental data and estimates of snail distributions in endemic and neighbouring areas. We applied Kriging with the aim of determining the spatial distribution of these two snail species and MaxEnt for modelling their ecological behaviour. Kriging showed that the North and the Centre of the MRR were generally either snail-free or contained only B. straminea, while both snail species could be found in the South. MaxEnt supported our observation that the northern and southern coastal regions were favoured by B. glabrata and diurnal mean temperature variation; July rainfall and November rainfall were the three variables favouring Biomphalaria breeding sites that contributed the most in the predictive model we developed. The study showed the location of areas suitable to Biomphalaria spp. and therefore at potential risk, first for invasion of these snails and later for the development of new schistosomiasis-endemic areas. This information should be useful, not only to estimate expansion possibilities of this disease in the MRR, but also to point out the climatic variables that would contribute to this expansion, thereby allowing timely application of prevention and control measures.

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Spatial distribution of Biomphalaria mollusks at São Francisco River Basin, Minas Gerais, Brazil, using geostatistical procedures

Cited by 34 publications

References 17 publications

A geoprocessing approach for studying and controlling schistosomiasis in the state of Minas Gerais, Brazil

A geoprocessing approach for studying and controlling schistosomiasis in the state of Minas Gerais, Brazil

Predicting the spatial distribution of Biomphalaria straminea, a potential intermediate host for Schistosoma mansoni, in China

Modelling of the distribution of Biomphalaria glabrata and Biomphalaria straminea in the metropolitan region of Recife, Pernambuco, Brazil

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