Time-specific ecologic niche models forecast the risk of hemorrhagic fever with renal syndrome in Dongting Lake district, China, 2005-2010.

Time-specific ecologic niche models forecast the risk of hemorrhagic fever with renal syndrome in Dongting Lake district, China, 2005-2010.

<h4>Background</h4>Hemorrhagic fever with renal syndrome (HFRS), a rodent-borne infectious disease, is one of the most serious public health threats in China. Increasing our understanding of the spatial and temporal patterns of HFRS infections could guide local prevention and control str...

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Autores principales: Hai-Ning Liu, Li-Dong Gao, Gerardo Chowell, Shi-Xiong Hu, Xiao-Ling Lin, Xiu-Jun Li, Gui-Hua Ma, Ru Huang, Hui-Suo Yang, Huaiyu Tian, Hong Xiao
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Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2014
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Acceso en línea:https://doaj.org/article/f9e38ba4d04d4c67a4bd0f14652378fd
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spelling oai:doaj.org-article:f9e38ba4d04d4c67a4bd0f14652378fd2021-11-25T06:02:08ZTime-specific ecologic niche models forecast the risk of hemorrhagic fever with renal syndrome in Dongting Lake district, China, 2005-2010.1932-620310.1371/journal.pone.0106839https://doaj.org/article/f9e38ba4d04d4c67a4bd0f14652378fd2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25184252/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Hemorrhagic fever with renal syndrome (HFRS), a rodent-borne infectious disease, is one of the most serious public health threats in China. Increasing our understanding of the spatial and temporal patterns of HFRS infections could guide local prevention and control strategies.<h4>Methodology/principal findings</h4>We employed statistical models to analyze HFRS case data together with environmental data from the Dongting Lake district during 2005-2010. Specifically, time-specific ecologic niche models (ENMs) were used to quantify and identify risk factors associated with HFRS transmission as well as forecast seasonal variation in risk across geographic areas. Results showed that the Maximum Entropy model provided the best predictive ability (AUC = 0.755). Time-specific Maximum Entropy models showed that the potential risk areas of HFRS significantly varied across seasons. High-risk areas were mainly found in the southeastern and southwestern areas of the Dongting Lake district. Our findings based on models focused on the spring and winter seasons showed particularly good performance. The potential risk areas were smaller in March, May and August compared with those identified for June, July and October to December. Both normalized difference vegetation index (NDVI) and land use types were found to be the dominant risk factors.<h4>Conclusions/significance</h4>Our findings indicate that time-specific ENMs provide a useful tool to forecast the spatial and temporal risk of HFRS.Hai-Ning LiuLi-Dong GaoGerardo ChowellShi-Xiong HuXiao-Ling LinXiu-Jun LiGui-Hua MaRu HuangHui-Suo YangHuaiyu TianHong XiaoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 9, p e106839 (2014)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Hai-Ning Liu
Li-Dong Gao
Gerardo Chowell
Shi-Xiong Hu
Xiao-Ling Lin
Xiu-Jun Li
Gui-Hua Ma
Ru Huang
Hui-Suo Yang
Huaiyu Tian
Hong Xiao
Time-specific ecologic niche models forecast the risk of hemorrhagic fever with renal syndrome in Dongting Lake district, China, 2005-2010.
description <h4>Background</h4>Hemorrhagic fever with renal syndrome (HFRS), a rodent-borne infectious disease, is one of the most serious public health threats in China. Increasing our understanding of the spatial and temporal patterns of HFRS infections could guide local prevention and control strategies.<h4>Methodology/principal findings</h4>We employed statistical models to analyze HFRS case data together with environmental data from the Dongting Lake district during 2005-2010. Specifically, time-specific ecologic niche models (ENMs) were used to quantify and identify risk factors associated with HFRS transmission as well as forecast seasonal variation in risk across geographic areas. Results showed that the Maximum Entropy model provided the best predictive ability (AUC = 0.755). Time-specific Maximum Entropy models showed that the potential risk areas of HFRS significantly varied across seasons. High-risk areas were mainly found in the southeastern and southwestern areas of the Dongting Lake district. Our findings based on models focused on the spring and winter seasons showed particularly good performance. The potential risk areas were smaller in March, May and August compared with those identified for June, July and October to December. Both normalized difference vegetation index (NDVI) and land use types were found to be the dominant risk factors.<h4>Conclusions/significance</h4>Our findings indicate that time-specific ENMs provide a useful tool to forecast the spatial and temporal risk of HFRS.
format article
author Hai-Ning Liu
Li-Dong Gao
Gerardo Chowell
Shi-Xiong Hu
Xiao-Ling Lin
Xiu-Jun Li
Gui-Hua Ma
Ru Huang
Hui-Suo Yang
Huaiyu Tian
Hong Xiao
author_facet Hai-Ning Liu
Li-Dong Gao
Gerardo Chowell
Shi-Xiong Hu
Xiao-Ling Lin
Xiu-Jun Li
Gui-Hua Ma
Ru Huang
Hui-Suo Yang
Huaiyu Tian
Hong Xiao
author_sort Hai-Ning Liu
title Time-specific ecologic niche models forecast the risk of hemorrhagic fever with renal syndrome in Dongting Lake district, China, 2005-2010.
title_short Time-specific ecologic niche models forecast the risk of hemorrhagic fever with renal syndrome in Dongting Lake district, China, 2005-2010.
title_full Time-specific ecologic niche models forecast the risk of hemorrhagic fever with renal syndrome in Dongting Lake district, China, 2005-2010.
title_fullStr Time-specific ecologic niche models forecast the risk of hemorrhagic fever with renal syndrome in Dongting Lake district, China, 2005-2010.
title_full_unstemmed Time-specific ecologic niche models forecast the risk of hemorrhagic fever with renal syndrome in Dongting Lake district, China, 2005-2010.
title_sort time-specific ecologic niche models forecast the risk of hemorrhagic fever with renal syndrome in dongting lake district, china, 2005-2010.
publisher Public Library of Science (PLoS)
publishDate 2014
url https://doaj.org/article/f9e38ba4d04d4c67a4bd0f14652378fd
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