Modeling and validation of environmental suitability for schistosomiasis transmission using remote sensing

Walz, Yvonne, Wegmann, Martin, Dech, Stefan, Vounatsou, Penelope, Poda, Jean-Noël, N'Goran, Eliézer K., Utzinger, Jürg and Raso, Giovanna, (2015). Modeling and validation of environmental suitability for schistosomiasis transmission using remote sensing. PLOS Neglected Tropical Diseases, 9(11), 1-22

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  • Sub-type Journal article
    Author Walz, Yvonne
    Wegmann, Martin
    Dech, Stefan
    Vounatsou, Penelope
    Poda, Jean-Noël
    N'Goran, Eliézer K.
    Utzinger, Jürg
    Raso, Giovanna
    Title Modeling and validation of environmental suitability for schistosomiasis transmission using remote sensing
    Appearing in PLOS Neglected Tropical Diseases
    Volume 9
    Issue No. 11
    Publication Date 2015-11
    Place of Publication Online
    Publisher PLOS
    Start page 1
    End page 22
    Language eng
    Abstract Schistosomiasis is a parasitic worm infection that is widespread in sub-Saharan Africa where people get in contact with open freshwater bodies. For many years, the strategy to control schistosomiasis was to prevent morbidity through deworming of school-aged children. Recently, transmission control has gained interest, which requires information where and when exactly transmission occurs. We investigated the potential of high-resolution remote sensing data to delineate potential transmission sites of schistosomiasis. Additionally, we characterized the habitat suitability for parasites and snails that are implicated in the schistosomiasis life cycle. Based on environmental field measurements in Burkina Faso and ecological data from the literature, functions of relative suitability were derived to determine the ecological relationship between the environment and snail and parasite fitness. These functions were employed to model the habitat suitability by using remote sensing variables that are aggregated to a habitat suitability index. We found that temporal dynamic of water bodies is one of the most relevant variables. Less relevant were topographic drainage lines. Our model also revealed significant relations with disease prevalence in different ecological zones of Côte d’Ivoire, and thus provides a useful tool to monitor new hotspots of disease transmission based on regularly updated remote sensing data.
    Keyword Remote Sensing
    Risk profiling
    Schistosomiasis
    Copyright Holder Walz et al.
    Copyright Year 2015
    Copyright type Creative commons
    DOI 10.1371/journal.pntd.0004217
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    Created: Wed, 09 Dec 2015, 01:54:39 JST by Sijia Yi on behalf of UNU EHS