Use of an ecologically relevant modelling approach to improve remote sensing-based schistosomiasis risk profiling

  • Yvonne Walz | walz@ehs.unu.edu Department of Remote Sensing, Institute for Geography and Geology, University of Würzburg, Würzburg; Institute for Environment and Human Security, United Nations University, Bonn, Germany. http://orcid.org/0000-0003-3781-5038
  • Martin Wegmann Department of Remote Sensing, Institute for Geography and Geology, University of Würzburg, Würzburg, Germany.
  • Benjamin Leutner Department of Remote Sensing, Institute for Geography and Geology, University of Würzburg, Würzburg, Germany.
  • Stefan Dech Department of Remote Sensing, Institute for Geography and Geology, University of Würzburg, Würzburg; German Remote Sensing Data Centre, German Aerospace Centre, Oberpfaffenhofen, Germany.
  • Penelope Vounatsou Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel; University of Basel, Basel, Switzerland.
  • Eliézer K. N'Goran Unité de Formation et de Recherche Biosciences, Université Félix Houphouët-Boigny, Abidjan; Centre Suisse de Recherches Scientifiques en Côte d’Ivoire, Abidjan, Côte d'Ivoire.
  • Giovanna Raso Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel; University of Basel, Basel, Switzerland.
  • Jürg Utzinger Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel; University of Basel, Basel, Switzerland.

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 Côte 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.

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Published
2015-11-30
Section
Original Articles
Keywords:
Spatial risk profiling, Schistosomiasis, Remote sensing, Ecological relevant model, Côte d’Ivoire
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How to Cite
Walz, Y., Wegmann, M., Leutner, B., Dech, S., Vounatsou, P., N’Goran, E., Raso, G., & Utzinger, J. (2015). Use of an ecologically relevant modelling approach to improve remote sensing-based schistosomiasis risk profiling. Geospatial Health, 10(2). https://doi.org/10.4081/gh.2015.398

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