@article{WalzWegmannLeutneretal.2015,
  author    = {Walz, Yvonne and Wegmann, Martin and Leutner, Benjamin and Dech, Stefan and Vounatsou, Penelope and N'Goran, Eli{\´e}zer K. and Raso, Giovanna and Utzinger, J{\"u}rg},
  title     = {Use of an ecologically relevant modelling approach to improve remote sensing-based schistosomiasis risk profiling},
  series = {Geospatial Health},
  volume    = {10},
  journal   = {Geospatial Health},
  number    = {2},
  doi       = {10.4081/gh.2015.398},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-126148},
  pages     = {398},
  year      = {2015},
  abstract  = {Schistosomiasis is a widespread water-based disease that puts close to 800 million people at risk of infection with more than 250 million infected, mainly in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and the frequency, duration and extent of human bodies exposed to infested water sources during human water contact. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. Since schistosomiasis risk profiling based on remote sensing data inherits a conceptual drawback if school-based disease prevalence data are directly related to the remote sensing measurements extracted at the location of the school, because the disease transmission usually does not exactly occur at the school, we took the local environment around the schools into account by explicitly linking ecologically relevant environmental information of potential disease transmission sites to survey measurements of disease prevalence. Our models were validated at two sites with different landscapes in C{\^o}te d'Ivoire using high- and moderateresolution remote sensing data based on random forest and partial least squares regression. We found that the ecologically relevant modelling approach explained up to 70\% of the variation in Schistosoma infection prevalence and performed better compared to a purely pixelbased modelling approach. Furthermore, our study showed that model performance increased as a function of enlarging the school catchment area, confirming the hypothesis that suitable environments for schistosomiasis transmission rarely occur at the location of survey measurements.},
  language  = {en}
}
@article{WalzWegmannDechetal.2015,
  author    = {Walz, Yvonne and Wegmann, Martin and Dech, Stefan and Vounastou, Penelope and Poda, Jean-Noel and N'Goran, Eli{\´e}zer K. and Raso, Giovanna and Utzinger, J{\"u}rg},
  title     = {Modeling and Validation of Environmental Suitability for Schistosomiasis Transmission Using Remote Sensing},
  series = {PLoS Neglected Tropical Diseases},
  volume    = {9},
  journal   = {PLoS Neglected Tropical Diseases},
  number    = {11},
  doi       = {10.1371/journal.pntd.0004217},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-125845},
  pages     = {e0004217},
  year      = {2015},
  abstract  = {Background Schistosomiasis is the most widespread water-based disease in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and human water contact patterns. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. We investigated the potential of remote sensing to characterize habitat conditions of parasite and intermediate host snails and discuss the relevance for public health. Methodology We employed high-resolution remote sensing data, environmental field measurements, and ecological data to model environmental suitability for schistosomiasis-related parasite and snail species. The model was developed for Burkina Faso using a habitat suitability index (HSI). The plausibility of remote sensing habitat variables was validated using field measurements. The established model was transferred to different ecological settings in C{\^o}te d'Ivoire and validated against readily available survey data from school-aged children. Principal Findings Environmental suitability for schistosomiasis transmission was spatially delineated and quantified by seven habitat variables derived from remote sensing data. The strengths and weaknesses highlighted by the plausibility analysis showed that temporal dynamic water and vegetation measures were particularly useful to model parasite and snail habitat suitability, whereas the measurement of water surface temperature and topographic variables did not perform appropriately. The transferability of the model showed significant relations between the HSI and infection prevalence in study sites of C{\^o}te d'Ivoire. Conclusions/Significance A predictive map of environmental suitability for schistosomiasis transmission can support measures to gain and sustain control. This is particularly relevant as emphasis is shifting from morbidity control to interrupting transmission. Further validation of our mechanistic model needs to be complemented by field data of parasite- and snail-related fitness. Our model provides a useful tool to monitor the development of new hotspots of potential schistosomiasis transmission based on regularly updated remote sensing data.},
  language  = {en}
}
@article{WalzWegmannDechetal.2015,
  author    = {Walz, Yvonne and Wegmann, Martin and Dech, Stefan and Raso, Giovanna and Utzinger, J{\"u}rg},
  title     = {Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook},
  series = {Parasites \& Vectors},
  volume    = {8},
  journal   = {Parasites \& Vectors},
  number    = {163},
  doi       = {10.1186/s13071-015-0732-6},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-148778},
  year      = {2015},
  abstract  = {Background: Schistosomiasis 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. Methods: We 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. Results: We 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. Conclusions: Our 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.},
  language  = {en}
}