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Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis
Schistosomiasis is a parasitic infection that is widespread in sub-Saharan Africa, where it represents a major health problem. We study the drivers of its geographical distribution in Senegal via a spatially explicit network model accounting for epidemiological dynamics driven by local socioeconomic...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5428445/ https://www.ncbi.nlm.nih.gov/pubmed/28352101 http://dx.doi.org/10.1038/s41598-017-00493-1 |
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author | Mari, Lorenzo Gatto, Marino Ciddio, Manuela Dia, Elhadji D. Sokolow, Susanne H. De Leo, Giulio A. Casagrandi, Renato |
author_facet | Mari, Lorenzo Gatto, Marino Ciddio, Manuela Dia, Elhadji D. Sokolow, Susanne H. De Leo, Giulio A. Casagrandi, Renato |
author_sort | Mari, Lorenzo |
collection | PubMed |
description | Schistosomiasis is a parasitic infection that is widespread in sub-Saharan Africa, where it represents a major health problem. We study the drivers of its geographical distribution in Senegal via a spatially explicit network model accounting for epidemiological dynamics driven by local socioeconomic and environmental conditions, and human mobility. The model is parameterized by tapping several available geodatabases and a large dataset of mobile phone traces. It reliably reproduces the observed spatial patterns of regional schistosomiasis prevalence throughout the country, provided that spatial heterogeneity and human mobility are suitably accounted for. Specifically, a fine-grained description of the socioeconomic and environmental heterogeneities involved in local disease transmission is crucial to capturing the spatial variability of disease prevalence, while the inclusion of human mobility significantly improves the explanatory power of the model. Concerning human movement, we find that moderate mobility may reduce disease prevalence, whereas either high or low mobility may result in increased prevalence of infection. The effects of control strategies based on exposure and contamination reduction via improved access to safe water or educational campaigns are also analyzed. To our knowledge, this represents the first application of an integrative schistosomiasis transmission model at a whole-country scale. |
format | Online Article Text |
id | pubmed-5428445 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-54284452017-05-15 Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis Mari, Lorenzo Gatto, Marino Ciddio, Manuela Dia, Elhadji D. Sokolow, Susanne H. De Leo, Giulio A. Casagrandi, Renato Sci Rep Article Schistosomiasis is a parasitic infection that is widespread in sub-Saharan Africa, where it represents a major health problem. We study the drivers of its geographical distribution in Senegal via a spatially explicit network model accounting for epidemiological dynamics driven by local socioeconomic and environmental conditions, and human mobility. The model is parameterized by tapping several available geodatabases and a large dataset of mobile phone traces. It reliably reproduces the observed spatial patterns of regional schistosomiasis prevalence throughout the country, provided that spatial heterogeneity and human mobility are suitably accounted for. Specifically, a fine-grained description of the socioeconomic and environmental heterogeneities involved in local disease transmission is crucial to capturing the spatial variability of disease prevalence, while the inclusion of human mobility significantly improves the explanatory power of the model. Concerning human movement, we find that moderate mobility may reduce disease prevalence, whereas either high or low mobility may result in increased prevalence of infection. The effects of control strategies based on exposure and contamination reduction via improved access to safe water or educational campaigns are also analyzed. To our knowledge, this represents the first application of an integrative schistosomiasis transmission model at a whole-country scale. Nature Publishing Group UK 2017-03-28 /pmc/articles/PMC5428445/ /pubmed/28352101 http://dx.doi.org/10.1038/s41598-017-00493-1 Text en © The Author(s) 2017 This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Mari, Lorenzo Gatto, Marino Ciddio, Manuela Dia, Elhadji D. Sokolow, Susanne H. De Leo, Giulio A. Casagrandi, Renato Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis |
title | Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis |
title_full | Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis |
title_fullStr | Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis |
title_full_unstemmed | Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis |
title_short | Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis |
title_sort | big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5428445/ https://www.ncbi.nlm.nih.gov/pubmed/28352101 http://dx.doi.org/10.1038/s41598-017-00493-1 |
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