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Modelling local areas of exposure to Schistosoma japonicum in a limited survey data environment

BACKGROUND: Spatial modelling studies of schistosomiasis (SCH) are now commonplace. Covariate values are commonly extracted at survey locations, where infection does not always take place, resulting in an unknown positional exposure mismatch. The present research aims to: (i) describe the nature of...

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Autores principales: Araujo Navas, Andrea L., Soares Magalhães, Ricardo J., Osei, Frank, Fornillos, Raffy Jay C., Leonardo, Lydia R., Stein, Alfred
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6090730/
https://www.ncbi.nlm.nih.gov/pubmed/30103810
http://dx.doi.org/10.1186/s13071-018-3039-6
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author Araujo Navas, Andrea L.
Soares Magalhães, Ricardo J.
Osei, Frank
Fornillos, Raffy Jay C.
Leonardo, Lydia R.
Stein, Alfred
author_facet Araujo Navas, Andrea L.
Soares Magalhães, Ricardo J.
Osei, Frank
Fornillos, Raffy Jay C.
Leonardo, Lydia R.
Stein, Alfred
author_sort Araujo Navas, Andrea L.
collection PubMed
description BACKGROUND: Spatial modelling studies of schistosomiasis (SCH) are now commonplace. Covariate values are commonly extracted at survey locations, where infection does not always take place, resulting in an unknown positional exposure mismatch. The present research aims to: (i) describe the nature of the positional exposure mismatch in modelling SCH helminth infections; (ii) delineate exposure areas to correct for such positional mismatch; and (iii) validate exposure areas using human positive cases. METHODS: To delineate exposure areas to Schistosoma japonicum, a spatial Bayesian network (sBN) was constructed. It uses data on exposure risk factors such as: potential sites for snails’ accessibility, geographical distribution of snail infection rate, and cost of the community to access nearby water bodies. Prior and conditional probabilities were obtained from the literature and inserted as weights based on their relative contribution to exposure; these probabilities were then used to calculate joint probabilities of exposure within the sBN. RESULTS: High values of probability of S. japonicum exposure correspond to polygons where snails could potentially be present, for instance in wet soils and areas with low slopes, but also where people can easily access water bodies. Low correlation (R(2) = 0.3) was found between the percentage of human cases and the delineated probabilities of exposure when validation buffers are generated over the human cases. CONCLUSIONS: The utility of a probabilistic method for the identification of exposure areas for S. japonicum, with wider application for other water-borne infections, was demonstrated. From a public health perspective, the schistosomiasis exposure sBN developed in this study could be used to guide local schistosomiasis control teams to specific potential areas of exposure, and improve efficiency of mass drug administration campaigns in places where people are likely to be exposed to the infection. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13071-018-3039-6) contains supplementary material, which is available to authorized users.
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spelling pubmed-60907302018-08-17 Modelling local areas of exposure to Schistosoma japonicum in a limited survey data environment Araujo Navas, Andrea L. Soares Magalhães, Ricardo J. Osei, Frank Fornillos, Raffy Jay C. Leonardo, Lydia R. Stein, Alfred Parasit Vectors Research BACKGROUND: Spatial modelling studies of schistosomiasis (SCH) are now commonplace. Covariate values are commonly extracted at survey locations, where infection does not always take place, resulting in an unknown positional exposure mismatch. The present research aims to: (i) describe the nature of the positional exposure mismatch in modelling SCH helminth infections; (ii) delineate exposure areas to correct for such positional mismatch; and (iii) validate exposure areas using human positive cases. METHODS: To delineate exposure areas to Schistosoma japonicum, a spatial Bayesian network (sBN) was constructed. It uses data on exposure risk factors such as: potential sites for snails’ accessibility, geographical distribution of snail infection rate, and cost of the community to access nearby water bodies. Prior and conditional probabilities were obtained from the literature and inserted as weights based on their relative contribution to exposure; these probabilities were then used to calculate joint probabilities of exposure within the sBN. RESULTS: High values of probability of S. japonicum exposure correspond to polygons where snails could potentially be present, for instance in wet soils and areas with low slopes, but also where people can easily access water bodies. Low correlation (R(2) = 0.3) was found between the percentage of human cases and the delineated probabilities of exposure when validation buffers are generated over the human cases. CONCLUSIONS: The utility of a probabilistic method for the identification of exposure areas for S. japonicum, with wider application for other water-borne infections, was demonstrated. From a public health perspective, the schistosomiasis exposure sBN developed in this study could be used to guide local schistosomiasis control teams to specific potential areas of exposure, and improve efficiency of mass drug administration campaigns in places where people are likely to be exposed to the infection. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13071-018-3039-6) contains supplementary material, which is available to authorized users. BioMed Central 2018-08-13 /pmc/articles/PMC6090730/ /pubmed/30103810 http://dx.doi.org/10.1186/s13071-018-3039-6 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.
spellingShingle Research
Araujo Navas, Andrea L.
Soares Magalhães, Ricardo J.
Osei, Frank
Fornillos, Raffy Jay C.
Leonardo, Lydia R.
Stein, Alfred
Modelling local areas of exposure to Schistosoma japonicum in a limited survey data environment
title Modelling local areas of exposure to Schistosoma japonicum in a limited survey data environment
title_full Modelling local areas of exposure to Schistosoma japonicum in a limited survey data environment
title_fullStr Modelling local areas of exposure to Schistosoma japonicum in a limited survey data environment
title_full_unstemmed Modelling local areas of exposure to Schistosoma japonicum in a limited survey data environment
title_short Modelling local areas of exposure to Schistosoma japonicum in a limited survey data environment
title_sort modelling local areas of exposure to schistosoma japonicum in a limited survey data environment
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6090730/
https://www.ncbi.nlm.nih.gov/pubmed/30103810
http://dx.doi.org/10.1186/s13071-018-3039-6
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