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

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          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% CrI − 1.57, − 0.09). A qualitatively similar association was found between NDVI and Biomphalaria sp. but was not statistically significant (log risk ratio − 1.42, 95% CrI − 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 < 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.

          Graphical Abstract

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13071-024-06353-y.

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          Most cited references37

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          SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty

          Abstract. SoilGrids produces maps of soil properties for the entire globe at medium spatial resolution (250 m cell size) using state-of-the-art machine learning methods to generate the necessary models. It takes as inputs soil observations from about 240 000 locations worldwide and over 400 global environmental covariates describing vegetation, terrain morphology, climate, geology and hydrology. The aim of this work was the production of global maps of soil properties, with cross-validation, hyper-parameter selection and quantification of spatially explicit uncertainty, as implemented in the SoilGrids version 2.0 product incorporating state-of-the-art practices and adapting them for global digital soil mapping with legacy data. The paper presents the evaluation of the global predictions produced for soil organic carbon content, total nitrogen, coarse fragments, pH (water), cation exchange capacity, bulk density and texture fractions at six standard depths (up to 200 cm). The quantitative evaluation showed metrics in line with previous global, continental and large-region studies. The qualitative evaluation showed that coarse-scale patterns are well reproduced. The spatial uncertainty at global scale highlighted the need for more soil observations, especially in high-latitude regions.
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            The 30 year TAMSAT African Rainfall Climatology And Time series (TARCAT) data set

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              Extension of the TAMSAT Satellite-Based Rainfall Monitoring over Africa and from 1983 to Present

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                Author and article information

                Contributors
                amberreed21@hotmail.com
                Journal
                Parasit Vectors
                Parasit Vectors
                Parasites & Vectors
                BioMed Central (London )
                1756-3305
                27 June 2024
                27 June 2024
                2024
                : 17
                : 272
                Affiliations
                [1 ]Lancaster Medical School, Lancaster University, ( https://ror.org/04f2nsd36) Bailrigg House, Bailrigg, Lancaster, LA1 4YE UK
                [2 ]Tropical Disease Biology, Liverpool School of Tropical Medicine, ( https://ror.org/03svjbs84) Pembroke Place, Liverpool, L3 5QA UK
                [3 ]GRID grid.415696.9, ISNI 0000 0004 0573 9824, Ministry of Health, ; 52367 Buraydah, Saudi Arabia
                [4 ]GRID grid.415487.b, ISNI 0000 0004 0598 3456, Malawi Liverpool Wellcome Trust Programme of Clinical Tropical Research, , Queen Elizabeth Central Hospital, College of Medicine, ; P. O. Box 30096, Blantyre, Malawi
                [5 ]Vector Biology, Liverpool School of Tropical Medicine, ( https://ror.org/03svjbs84) Pembroke Place, Liverpool, L3 5QA UK
                [6 ]Mathematics and Statistics, Lancaster University, ( https://ror.org/04f2nsd36) Bailrigg House, Bailrigg, Lancaster, LA1 4YE UK
                Article
                6353
                10.1186/s13071-024-06353-y
                11209974
                38937778
                9338ac4b-0b64-4a05-b1e3-bc3a58d3a865
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 11 February 2024
                : 12 June 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MR/N013514/1
                Award Recipient :
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Parasitology
                bulinus,biomphalaria,snail abundance,bayesian multilevel models,geospatial analysis,gaussian latent process,remote sensing

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