Table 1.

Characteristics of studies using GIS, RS and spatial analysis for the mapping and prediction of human schistosomiasis and transmission modelling

			Data sources and parameters for GIS
Study aim	Study area	Spatial scale	RS environmental data	Demographic, epidemiological and socio-economic data	Spatial analysis	Reference
To determine the relative risk of schistosomiasis for interactive analysis and planning of control activities	Nile delta, Egypt	Meso	Tmax, Tmin, dT	Prevalence data of S. haematobium and S. mansoni	Exploratory data analysis	Malone et al. (1994)
To develop suitability maps for S. haematobium and S. mansoni at a national scale	Zimbabwe	Meso	NDVI, Tmax, Tmin	Prevalence data of S. haematobium and S. mansoni	Visualization/mapping	Mukaratirwa et al. (1999)
To collate available survey data in a single database to (1) describe schistosome prevalence across Africa, and (2) highlight areas for which further information is required	Sub-Saharan Africa	Macro	n.d.	Prevalence data of S. haemabobium and S. mansoni	Visualization/mapping	Brooker et al. (2000)
To predict the risk of schistosomiasis and to initiate the use of the resulting GIS model for integrated schistosomiasis control	Kafr El-Sheikh governorate, Egypt	Micro-meso	NDVI, Tmax, Tmin, dT	Prevalence data of S. mansoni	Exploratory data analysis and modelling	Abdel-Rahmanet al. (2001)
To develop schistosome prediction maps for spatial targeting of mass drug administration	Tanzania	Meso	LST, NDVI	Prevalence data of S. haematobium (reported ‘blood in urine’ used as a proxy measure)	Exploratory data analysis and modelling	Brooker et al. (2001)
To model the distribution of S. haematobium across the country	Cameroon	Macro	LST, rainfall	Prevalence data of S. haematobium	Exploratory data analysis and modelling	Brooker et al. (2002b)
To develop temperature-suitability maps for schistosomiasis in South Africa	South Africa	Macro	Mdx, Mdn, temperature	Prevalence data of S. mansoni and S. haematobium; population data	Exploratory data analysis and modelling	Moodley et al. (2003)
To examine the spatial distribution of S. mansoni in a single village	Fagnampleu, Man region, Côte d'Ivoire	Micro	n.d.	Prevalence data of S. mansoni	Exploratory data analysis	Utzinger et al. (2003)
To identify risk factors explaining the geographical distribution of S. mansoni infections in the mountainous region of Man	Man region, Côte d'Ivoire	Meso	LST, NDVI, rainfall	Prevalence data of S. mansoni	Exploratory data analysis and modelling	Raso et al. (2005)
To model the distribution of S. mansoni and intermediate host snails in Uganda using satellite sensor data and GIS	Uganda	Macro	LST, NDVI	Prevalence data of S. mansoni; Biomphalaria spp. snail data	Exploratory data analysis and modelling	Stensgaard et al. (2005)
To investigate spatial patterns of urinary schistosomiasis infection in a highly endemic area of coastal Kenya	Msambweni division, Kenya	Micro	Water chemistry, distance to water bodies	Prevalence data of S. haematobium; age	Exploratory data analysis	Clennon et al. (2006)
To model the prevalence of schistosome infections using a Bayesian approach	NW Tanzania	Macro	LST, distance to water bodies	Prevalence data of S. mansoni and S. haematobium	Exploratory data analysis and modelling	Clementset al. (2006a)
To model the regional distribution of intensity of S. mansoni infection using a Bayesian approach	East Africa	Macro	Elevation, distance to water bodies	Intensity data of S. mansoni	Exploratory data analysis and modelling	Clementset al. (2006b)
To model the distribution of schistosomiasis based on a national questionnaire survey and using a Bayesian approach	Tanzania	Macro	LST, NDVI, elevation	Prevalence of reported schistosomiasis	Exploratory data analysis and modelling	Clementset al. (2008a)
To model S. haematobium prevalence and to predict the underlying uncertainty of prediction using a Bayesian approach	West Africa	Macro	LST, distance to water bodies, age, sex	Prevalence data of S. haematobium	Logistic regression analysis	Clementset al. (2008b)

dT, temperature difference; LST, land surface temperature; Mdn, mean daily minimum temperature; Mdx, mean daily maximum temperature; n.d., not determined; NDVI, normalized difference vegetation index; T_max, maximum temperature; T_min, minimum temperature.