Remote sensing, geographical information system and spatial analysis for schistosomiasis epidemiology and ecology in Africa

Abstract

SummaryBeginning in 1970, the potential of remote sensing (RS) techniques, coupled with geographical information systems (GIS), to improve our understanding of the epidemiology and control of schistosomiasis in Africa, has steadily grown. In our current review, working definitions of RS, GIS and spatial analysis are given, and applications made to date with RS and GIS for the epidemiology and ecology of schistosomiasis in Africa are summarised. Progress has been made in mapping the prevalence of infection in humans and the distribution of intermediate host snails. More recently, Bayesian geostatistical modelling approaches have been utilized for predicting the prevalence and intensity of infection at different scales. However, a number of challenges remain; hence new research is needed to overcome these limitations. First, greater spatial and temporal resolution seems important to improve risk mapping and understanding of transmission dynamics at the local scale. Second, more realistic risk profiling can be achieved by taking into account information on people's socio-economic status; furthermore, future efforts should incorporate data on domestic access to clean water and adequate sanitation, as well as behavioural and educational issues. Third, high-quality data on intermediate host snail distribution should facilitate validation of infection risk maps and modelling transmission dynamics. Finally, more emphasis should be placed on risk mapping and prediction of multiple species parasitic infections in an effort to integrate disease risk mapping and to enhance the cost-effectiveness of their control.

Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling

“…The first detailed hard-copy maps of the global distribution of schistosomiasis were published a quarter century ago (Doumenge et al, 1987). With the advent of desktop GIS and mapping software, the application of GIS for creating digital disease maps became feasible and these proved extremely useful for the planning, control and surveillance of human helminth infections (Brooker and Michael, 2000;Brooker et al, , 2009Brooker et al, , 2010Simoonga et al, 2009). However, with the exception of a single state, Ogun (Ekpo et al, 2008) and despite several surveys pertaining to the distribution of schistosomiasis and the intermediate host snails in Nigeria, the first ones dating back to the 1930s (Ramsay, 1934;Cowper 1963Cowper , 1973WHO, 1985), the compilation of these records into a comprehensive GIS-based overview did not exist before the current study.…”

Geospatial (s)tools: integration of advanced epidemiological sampling and novel diagnostics

“…Geospatial tools, including geographical information systems (GIS) and satellite-based technologies such as remote sensing and global positioning systems (GPS), coupled with geostatistical approaches, are increasingly and successfully applied at different levels from sampling to risk profiling of parasitic diseases (Rinaldi et al, 2006;Brooker, 2007;Simoonga et al, 2009;Machault et al, 2011;Utzinger et al, 2011). Indeed, this represents an innovative and useful way to communicate finding to field researchers and decision-makers and it is a powerful approach that also addresses the spatial targeting of parasite control, including the choice of treatment to be applied.…”

“…Geostatistical methods that take into account ecology and epidemiology of parasites and vectors/intermediate hosts have been discussed for malaria, soil-transmitted helminthiasis, schistosomiasis and other parasitic infections and proven to be an attractive model to predict parasite distribution and subsequently guide public health interventions (Brooker, 2007;Simoonga et al, 2009;Machault et al, 2011;Patil et al, 2011;Pullan et al, 2012). A promising approach to sampling inherited from veterinary parasitology is application of pooling of biological samples, such as blood, faeces and urine (Whittington et al, 2000;Mekonnen et al, 2013).…”

Fine-scale mapping of vector habitats using very high resolution satellite imagery: a liver fluke case-study

“…the study by McCann et al (2010) where the risk was estimated using post codes with a mean surface area of 2,000 km 2 . Mapping potential habitats at a finer spatial scale could substantially improve the temporal and spatial resolution of current risk maps and create novel possibilities for improved disease management based on better understanding of transmission dynamics at the local habitat scale (Lacaux et al, 2007;Simoonga et al, 2009;Charlier et al, 2011;Estallo et al, 2012). Flexible, automated and operational tools capable of characterising vector habitats at high resolutions are, however, currently lacking.…”