A geospatial analysis of local intermediate snail host distributions provides insight into schistosomiasis risk within under-sampled areas of southern Lake Malawi

A geospatial analysis of local intermediate snail host distributions provides insight into schistosomiasis risk within under-sampled areas of southern Lake Malawi | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A geospatial analysis of local intermediate snail host distributions provides insight into schistosomiasis risk within under-sampled areas of southern Lake Malawi Amber Lydia Reed, Mohammad H Al-Harbi, Peter Makaulai, Charlotte Condemine, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3949127/v3 This work is licensed under a CC BY 4.0 License Status: Under Review Version 3 posted 4 You are reading this latest preprint version Show more versions Abstract Background : Along the southern shoreline of Lake Malawi, the incidence of schistosomiasis is increasing with snails of the genera Bulinus and Biomphalaria transmitting urogenital and intestinal schistosomiasis, respectively. Since the underlying distribution of snails is partially known, often being focal, developing pragmatic spatial models that interpolate snail information across under-sampled regions is required to understand and assess current and future risk of schistosomiasis. Methods : A secondary geospatial analysis of recently collected malacological and environmental survey data was undertaken. Using a Bayesian Poisson latent Gaussian process model, abundance data were fitted for Bulinus and Biomphalaria . Interpolating the abundance of snails along the shoreline (given their relative distance along the shoreline) was achieved by smoothing, using extracted environmental rainfall, land surface temperature (LST), evapotranspiration, normalised difference vegetation index (NDVI) and soil type covariate data for all predicted locations. Our adopted model used a combination of two-dimensional (2D) and one dimensional (1D) mapping. Results: A significant association between normalised difference vegetation index (NDVI) and abundance of Bulinus spp. was detected [log risk ratio -0.83, 95% CI: -1.57, -0.09]. A qualitatively similar association was found between NDVI and Biomphalaria spp. but was not statistically significant [log risk ratio -1.42, 95% CI:-3.09, 0.10]. Analyses of all other environmental data were considered non-significant. Conclusions: The spatial range in which interpolation of snail distributions is possible appears less than 10km, owing to fine-scale biotic and abiotic heterogeneities. The forthcoming challenge is to refine geospatial sampling frameworks with future opportunities to map schistosomiasis within actual or predicted snail distributions. In so doing, this would better reveal local environmental transmission possibilities. Population Biology Bulinus Biomphalaria Snail abundance Bayesian multilevel models Geospatial analysis Gaussian latent process Remote sensing Full Text Supplementary Files Additionalfile1.xls Additionalfile2.pdf Additionalfile3.pdf Additionalfile4.pdf Additionalfile5.pdf Additionalfile6.pdf Additionalfile7.pdf Additionalfile8.pdf Additionalfile9.pdf Additionalfile10.pdf Cite Share Download PDF Status: Under Review Version 3 posted Editorial decision: Revision requested 10 Jun, 2024 Editor assigned by journal 10 Jun, 2024 Submission checks completed at journal 15 May, 2024 First submitted to journal 14 May, 2024 You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3949127","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312688264,"identity":"a4d024da-272f-11ef-91e4-06cc9d20a69f","order_by":0,"name":"Amber Lydia 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Since the underlying distribution of snails is partially known, often being focal, developing pragmatic spatial models that interpolate snail information across under-sampled regions is required to understand and assess current and future risk of schistosomiasis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A secondary geospatial analysis of recently collected malacological and environmental survey data was undertaken. Using a Bayesian Poisson latent Gaussian process model, abundance data were fitted for \u003cem\u003eBulinus \u003c/em\u003eand \u003cem\u003eBiomphalaria\u003c/em\u003e. Interpolating the abundance of snails along the shoreline (given their relative distance along the shoreline) was achieved by smoothing, using extracted environmental rainfall, land surface temperature (LST), evapotranspiration, normalised difference vegetation index (NDVI) and soil type covariate data for all predicted locations. Our adopted model used a combination of two-dimensional (2D) and one dimensional (1D) mapping.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA significant association between normalised difference vegetation index (NDVI) and abundance of \u003cem\u003eBulinus \u003c/em\u003espp. was detected [log risk ratio -0.83, 95% CI: -1.57, -0.09]. A qualitatively similar association was found between NDVI and \u003cem\u003eBiomphalaria \u003c/em\u003espp. but was not statistically significant [log risk ratio -1.42, 95% CI:-3.09, 0.10]. Analyses of all other environmental data were considered non-significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe spatial range in which interpolation of snail distributions is possible appears less than 10km, owing to fine-scale biotic and abiotic heterogeneities. The forthcoming challenge is to refine geospatial sampling frameworks with future opportunities to map schistosomiasis within actual or predicted snail distributions. 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