Geographic information systems as a tool for control program management for schistosomiasis in Egypt

Abdelrahman, Mahmoud A. E.; El-Bahy, Mohamed M.; Malone, John B.; Thompson, R. A.; Bahy, N.M. El

doi:10.1016/s0001-706x(01)00102-4

Cited by 29 publications

(9 citation statements)

References 6 publications

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“…The potential of remote sensing and its combination with geographical information system (GIS)-based spatial analyses for schistosomiasis risk profiling can be summarised by the ability (i) to determine the geographical limit of disease distribution due to ecological constraints of disease transmission; (ii) to further investigate the context of disease ecology and epidemiology through its spatial relation; (iii) to support prevention, surveillance and control through prioritising areas of disease risk; and (iv) to provide early warning for areas where disease transmission could become established [ 4 , 9 , 11 , 80 , 109 - 112 ]. However, most studies had an integrative focus combining some of the different analytical steps from spatial disease delineation to early warning mentioned above.…”

Section: Introductionmentioning

confidence: 99%

Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook

et al. 2015

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BackgroundSchistosomiasis is a water-based disease that affects an estimated 250 million people, mainly in sub-Saharan Africa. The transmission of schistosomiasis is spatially and temporally restricted to freshwater bodies that contain schistosome cercariae released from specific snails that act as intermediate hosts. Our objective was to assess the contribution of remote sensing applications and to identify remaining challenges in its optimal application for schistosomiasis risk profiling in order to support public health authorities to better target control interventions.MethodsWe reviewed the literature (i) to deepen our understanding of the ecology and the epidemiology of schistosomiasis, placing particular emphasis on remote sensing; and (ii) to fill an identified gap, namely interdisciplinary research that bridges different strands of scientific inquiry to enhance spatially explicit risk profiling. As a first step, we reviewed key factors that govern schistosomiasis risk. Secondly, we examined remote sensing data and variables that have been used for risk profiling of schistosomiasis. Thirdly, the linkage between the ecological consequence of environmental conditions and the respective measure of remote sensing data were synthesised.ResultsWe found that the potential of remote sensing data for spatial risk profiling of schistosomiasis is – in principle – far greater than explored thus far. Importantly though, the application of remote sensing data requires a tailored approach that must be optimised by selecting specific remote sensing variables, considering the appropriate scale of observation and modelling within ecozones. Interestingly, prior studies that linked prevalence of Schistosoma infection to remotely sensed data did not reflect that there is a spatial gap between the parasite and intermediate host snail habitats where disease transmission occurs, and the location (community or school) where prevalence measures are usually derived from.ConclusionsOur findings imply that the potential of remote sensing data for risk profiling of schistosomiasis and other neglected tropical diseases has yet to be fully exploited.

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Section: Introductionmentioning

confidence: 99%

Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook

et al. 2015

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“…GIS has also been used to study schistosomiasis in several countries: in Asia (Cross et al 1996), Brazil (Bavia et al 2001, 2008, Martins-Bedé et al 2009), Chad , China , Seto et al 2002, Yang et al 2005, Egypt (Malone et al 1994, 1997, Abdel-Rahman et al 2001, Ethiopia , Philippines (Cross et al 1996), Tanzania (Brooker et al 2001) and Uganda (Kabatereine et al 2004).…”

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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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“…The influence of climate and environmental variables to the distribution of schistosomiasis was documented by Brown (1994) and Appleton (1978). The use of GIS for the study of schistosomiasis was also done in several other countries: in Asia (Cross et al 1996), China , Seto et al 2002, Yang et al 2005, Ethiopia , Egypt (Malone et al 1994, 1997, Abdel-Rahman et al 2001, Uganda (Kabatereine et al 2004), Tanzania (Brooker et al 2001), Chad . In Brazil, one of the first studies trying to correlate disease distribution with environmental variables was conducted by Bavia et al (2001), in Bahia.…”

mentioning

confidence: 99%

Analysis and estimative of schistosomiasis prevalence for the state of Minas Gerais, Brazil, using multiple regression with social and environmental spatial data

Guimarães

Freitas

Dutra

et al. 2006

Mem. Inst. Oswaldo Cruz

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Geographic information systems as a tool for control program management for schistosomiasis in Egypt

Cited by 29 publications

References 6 publications

Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook

Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook

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

Analysis and estimative of schistosomiasis prevalence for the state of Minas Gerais, Brazil, using multiple regression with social and environmental spatial data

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