Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis

Abstract 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 soci...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Lorenzo Mari, Marino Gatto, Manuela Ciddio, Elhadji D. Dia, Susanne H. Sokolow, Giulio A. De Leo, Renato Casagrandi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9c1f99e19d0746d5a4d70263c30495d1
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:9c1f99e19d0746d5a4d70263c30495d1
record_format	dspace
spelling	oai:doaj.org-article:9c1f99e19d0746d5a4d70263c30495d12021-12-02T16:07:57ZBig-data-driven modeling unveils country-wide drivers of endemic schistosomiasis10.1038/s41598-017-00493-12045-2322https://doaj.org/article/9c1f99e19d0746d5a4d70263c30495d12017-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-00493-1https://doaj.org/toc/2045-2322Abstract 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.Lorenzo MariMarino GattoManuela CiddioElhadji D. DiaSusanne H. SokolowGiulio A. De LeoRenato CasagrandiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lorenzo Mari Marino Gatto Manuela Ciddio Elhadji D. Dia Susanne H. Sokolow Giulio A. De Leo Renato Casagrandi Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis
description	Abstract 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	article
author	Lorenzo Mari Marino Gatto Manuela Ciddio Elhadji D. Dia Susanne H. Sokolow Giulio A. De Leo Renato Casagrandi
author_facet	Lorenzo Mari Marino Gatto Manuela Ciddio Elhadji D. Dia Susanne H. Sokolow Giulio A. De Leo Renato Casagrandi
author_sort	Lorenzo Mari
title	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_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_sort	big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/9c1f99e19d0746d5a4d70263c30495d1
work_keys_str_mv	AT lorenzomari bigdatadrivenmodelingunveilscountrywidedriversofendemicschistosomiasis AT marinogatto bigdatadrivenmodelingunveilscountrywidedriversofendemicschistosomiasis AT manuelaciddio bigdatadrivenmodelingunveilscountrywidedriversofendemicschistosomiasis AT elhadjiddia bigdatadrivenmodelingunveilscountrywidedriversofendemicschistosomiasis AT susannehsokolow bigdatadrivenmodelingunveilscountrywidedriversofendemicschistosomiasis AT giulioadeleo bigdatadrivenmodelingunveilscountrywidedriversofendemicschistosomiasis AT renatocasagrandi bigdatadrivenmodelingunveilscountrywidedriversofendemicschistosomiasis
_version_	1718384674249637888

Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis

Ejemplares similares