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dc.contributor.authorWalz, Yvonneen_US
dc.contributor.authorKoch, Magalyen_US
dc.contributor.authorBiritwum, Nana-Kwadwoen_US
dc.contributor.authorUtzinger, Jurgen_US
dc.contributor.authorKulinkina, Alexandra V.en_US
dc.contributor.authorNaumova, Elena N.en_US
dc.date.accessioned2018-10-24T19:04:55Z
dc.date.available2018-10-24T19:04:55Z
dc.date.issued2018-06-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000437442000022&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e74115fe3da270499c3d65c9b17d654
dc.identifier.citationKulinkina AV, Walz Y, Koch M, Biritwum N-K, Utzinger J, Naumova EN (2018) Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles. PLoS Negl Trop Dis 12(6): e0006517. https://doi.org/10.1371/journal.pntd.0006517
dc.identifier.issn1935-2735
dc.identifier.urihttps://hdl.handle.net/2144/31495
dc.description.abstractBACKGROUND: Schistosomiasis is a water-related neglected tropical disease. In many endemic low- and middle-income countries, insufficient surveillance and reporting lead to poor characterization of the demographic and geographic distribution of schistosomiasis cases. Hence, modeling is relied upon to predict areas of high transmission and to inform control strategies. We hypothesized that utilizing remotely sensed (RS) environmental data in combination with water, sanitation, and hygiene (WASH) variables could improve on the current predictive modeling approaches. METHODOLOGY: Schistosoma haematobium prevalence data, collected from 73 rural Ghanaian schools, were used in a random forest model to investigate the predictive capacity of 15 environmental variables derived from RS data (Landsat 8, Sentinel-2, and Global Digital Elevation Model) with fine spatial resolution (10-30 m). Five methods of variable extraction were tested to determine the spatial linkage between school-based prevalence and the environmental conditions of potential transmission sites, including applying the models to known human water contact locations. Lastly, measures of local water access and groundwater quality were incorporated into RS-based models to assess the relative importance of environmental and WASH variables. PRINCIPAL FINDINGS: Predictive models based on environmental characterization of specific locations where people contact surface water bodies offered some improvement as compared to the traditional approach based on environmental characterization of locations where prevalence is measured. A water index (MNDWI) and topographic variables (elevation and slope) were important environmental risk factors, while overall, groundwater iron concentration predominated in the combined model that included WASH variables. CONCLUSIONS/SIGNIFICANCE: The study helps to understand localized drivers of schistosomiasis transmission. Specifically, unsatisfactory water quality in boreholes perpetuates reliance on surface water bodies, indirectly increasing schistosomiasis risk and resulting in rapid reinfection (up to 40% prevalence six months following preventive chemotherapy). Considering WASH-related risk factors in schistosomiasis prediction can help shift the focus of control strategies from treating symptoms to reducing exposure.en_US
dc.description.sponsorshipThis study was funded in part by the National Institutes of Health (R34 AI097083-01A1), Tufts Institute for Innovation, Jonathan M. Tisch College of Civic Life, Natalie V. Zucker, Charlton, Tufts Collaborates, and Tufts Innovates grants. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. (R34 AI097083-01A1 - National Institutes of Health; Tufts Institute for Innovation; Jonathan M. Tisch College of Civic Life; Charlton; Tufts Collaborates; Tufts Innovates; Natalie V. Zucker grant)en_US
dc.languageEnglish
dc.publisherPUBLIC LIBRARY SCIENCEen_US
dc.relation.ispartofPLOS NEGLECTED TROPICAL DISEASES
dc.relation.isversionofhttps://doi.org/10.1371/journal.pntd.0006517
dc.rightsCopyright: © 2018 Kulinkina et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectParasitologyen_US
dc.subjectAnimalsen_US
dc.subjectChilden_US
dc.subjectFemaleen_US
dc.subjectGeographyen_US
dc.subjectGhanaen_US
dc.subjectHumansen_US
dc.subjectHygieneen_US
dc.subjectMaleen_US
dc.subjectPrevalenceen_US
dc.subjectSanitationen_US
dc.subjectSchistosoma haematobiumen_US
dc.subjectSchistosomiasis haematobiaen_US
dc.subjectSchoolsen_US
dc.subjectWateren_US
dc.subjectScience & technologyen_US
dc.subjectLife sciences & biomedicineen_US
dc.subjectInfectious diseasesen_US
dc.subjectTropical medicineen_US
dc.subjectAdjusted vegetation indexen_US
dc.subjectInformation systemsen_US
dc.subjectRandom foresten_US
dc.subjectEpidemiologyen_US
dc.subjectInfectionen_US
dc.subjectRisken_US
dc.subjectFeaturesen_US
dc.subjectEcologyen_US
dc.subjectAfricaen_US
dc.subjectCross-sectional studiesen_US
dc.subjectModels, statisticalen_US
dc.subjectRemote sensing technologyen_US
dc.subjectWater qualityen_US
dc.subjectBiological sciencesen_US
dc.subjectMedical and health sciencesen_US
dc.titleImproving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profilesen_US
dc.typeArticleen_US
dc.identifier.doi10.1371/journal.pntd.0006517
pubs.elements-sourceweb-of-scienceen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.publication-statusPublisheden_US
dc.identifier.orcid0000-0002-6186-1619 (Koch, Magaly)


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Copyright: © 2018 Kulinkina et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Except where otherwise noted, this item's license is described as Copyright: © 2018 Kulinkina et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.