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Identifying and Predicting the Geographical Distribution Patterns of Oncomelania hupensis

Schistosomiasis is a snail-borne parasitic disease endemic to the tropics and subtropics, whose distribution depends on snail prevalence as determined by climatic and environmental factors. Here, dynamic spatial and temporal patterns of Oncomelania hupensis distributions were quantified using genera...

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Autores principales: Niu, Yingnan, Li, Rendong, Qiu, Juan, Xu, Xingjian, Huang, Duan, Shao, Qihui, Cui, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616429/
https://www.ncbi.nlm.nih.gov/pubmed/31234446
http://dx.doi.org/10.3390/ijerph16122206
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author Niu, Yingnan
Li, Rendong
Qiu, Juan
Xu, Xingjian
Huang, Duan
Shao, Qihui
Cui, Ying
author_facet Niu, Yingnan
Li, Rendong
Qiu, Juan
Xu, Xingjian
Huang, Duan
Shao, Qihui
Cui, Ying
author_sort Niu, Yingnan
collection PubMed
description Schistosomiasis is a snail-borne parasitic disease endemic to the tropics and subtropics, whose distribution depends on snail prevalence as determined by climatic and environmental factors. Here, dynamic spatial and temporal patterns of Oncomelania hupensis distributions were quantified using general statistics, global Moran’s I, and standard deviation ellipses, with Maxent modeling used to predict the distribution of habitat areas suitable for this snail in Gong’an County, a severely affected region of Jianghan Plain, China, based on annual average temperature, humidity of the climate, soil type, normalized difference vegetation index, land use, ditch density, land surface temperature, and digital elevation model variables; each variable’s contribution was tested using the jackknife method. Several key results emerged. First, coverage area of O. hupensis had changed little from 2007 to 2012, with some cities, counties, and districts alternately increasing and decreasing, with ditch and bottomland being the main habitat types. Second, although it showed a weak spatial autocorrelation, changing negligibly, there was a significant east–west gradient in the O. hupensis habitat area. Third, 21.9% of Gong’an County’s area was at high risk of snail presence; and ditch density, temperature, elevation, and wetting index contributed most to their occurrence. Our findings and methods provide valuable and timely insight for the control, monitoring, and management of schistosomiasis in China.
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spelling pubmed-66164292019-07-18 Identifying and Predicting the Geographical Distribution Patterns of Oncomelania hupensis Niu, Yingnan Li, Rendong Qiu, Juan Xu, Xingjian Huang, Duan Shao, Qihui Cui, Ying Int J Environ Res Public Health Article Schistosomiasis is a snail-borne parasitic disease endemic to the tropics and subtropics, whose distribution depends on snail prevalence as determined by climatic and environmental factors. Here, dynamic spatial and temporal patterns of Oncomelania hupensis distributions were quantified using general statistics, global Moran’s I, and standard deviation ellipses, with Maxent modeling used to predict the distribution of habitat areas suitable for this snail in Gong’an County, a severely affected region of Jianghan Plain, China, based on annual average temperature, humidity of the climate, soil type, normalized difference vegetation index, land use, ditch density, land surface temperature, and digital elevation model variables; each variable’s contribution was tested using the jackknife method. Several key results emerged. First, coverage area of O. hupensis had changed little from 2007 to 2012, with some cities, counties, and districts alternately increasing and decreasing, with ditch and bottomland being the main habitat types. Second, although it showed a weak spatial autocorrelation, changing negligibly, there was a significant east–west gradient in the O. hupensis habitat area. Third, 21.9% of Gong’an County’s area was at high risk of snail presence; and ditch density, temperature, elevation, and wetting index contributed most to their occurrence. Our findings and methods provide valuable and timely insight for the control, monitoring, and management of schistosomiasis in China. MDPI 2019-06-21 2019-06 /pmc/articles/PMC6616429/ /pubmed/31234446 http://dx.doi.org/10.3390/ijerph16122206 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Niu, Yingnan
Li, Rendong
Qiu, Juan
Xu, Xingjian
Huang, Duan
Shao, Qihui
Cui, Ying
Identifying and Predicting the Geographical Distribution Patterns of Oncomelania hupensis
title Identifying and Predicting the Geographical Distribution Patterns of Oncomelania hupensis
title_full Identifying and Predicting the Geographical Distribution Patterns of Oncomelania hupensis
title_fullStr Identifying and Predicting the Geographical Distribution Patterns of Oncomelania hupensis
title_full_unstemmed Identifying and Predicting the Geographical Distribution Patterns of Oncomelania hupensis
title_short Identifying and Predicting the Geographical Distribution Patterns of Oncomelania hupensis
title_sort identifying and predicting the geographical distribution patterns of oncomelania hupensis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616429/
https://www.ncbi.nlm.nih.gov/pubmed/31234446
http://dx.doi.org/10.3390/ijerph16122206
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