• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

基于环境和社会经济因素的人类西尼罗河病毒(WNV)风险预测图。

Predictive mapping of human risk for West Nile virus (WNV) based on environmental and socioeconomic factors.

机构信息

Suffolk County Vector Control, Yaphank, New York, United States of America.

出版信息

PLoS One. 2011;6(8):e23280. doi: 10.1371/journal.pone.0023280. Epub 2011 Aug 10.

DOI:10.1371/journal.pone.0023280
PMID:21853103
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3154328/
Abstract

A West Nile virus (WNV) human risk map was developed for Suffolk County, New York utilizing a case-control approach to explore the association between the risk of vector-borne WNV and habitat, landscape, virus activity, and socioeconomic variables derived from publically available datasets. Results of logistic regression modeling for the time period between 2000 and 2004 revealed that higher proportion of population with college education, increased habitat fragmentation, and proximity to WNV positive mosquito pools were strongly associated with WNV human risk. Similar to previous investigations from north-central US, this study identified middle class suburban neighborhoods as the areas with the highest WNV human risk. These results contrast with similar studies from the southern and western US, where the highest WNV risk was associated with low income areas. This discrepancy may be due to regional differences in vector ecology, urban environment, or human behavior. Geographic Information Systems (GIS) analytical tools were used to integrate the risk factors in the 2000-2004 logistic regression model generating WNV human risk map. In 2005-2010, 41 out of 46 (89%) of WNV human cases occurred either inside of (30 cases) or in close proximity (11 cases) to the WNV high risk areas predicted by the 2000-2004 model. The novel approach employed by this study may be implemented by other municipal, local, or state public health agencies to improve geographic risk estimates for vector-borne diseases based on a small number of acute human cases.

摘要

一个西尼罗河病毒(WNV)人类风险图为纽约州萨福克县开发,利用病例对照方法探讨与蚊媒WNV的风险之间的关系,从公共可用的数据集派生的栖息地,景观,病毒活动和社会经济变量。逻辑回归模型的结果,用于 2000 年至 2004 年期间,表明较高比例的具有大学教育的人口,增加的生境破碎化,以及接近WNV阳性蚊子池与 WNV 人类风险强烈相关。与来自美国中北部的先前调查类似,这项研究确定中产阶级郊区社区为WNV 人类风险最高的地区。这些结果与来自美国南部和西部的类似研究形成对比,在这些研究中,最高的 WNV 风险与低收入地区有关。这种差异可能是由于区域间的蚊虫生态,城市环境或人类行为的差异所致。地理信息系统(GIS)分析工具用于整合 2000-2004 年逻辑回归模型中的风险因素,生成 WNV 人类风险图。在 2005-2010 年期间,46 例 WNV 人类病例中的 41 例(89%)发生在 2000-2004 年模型预测的 WNV 高风险区域内(30 例)或附近(11 例)。本研究采用的新方法可以由其他市,地方或州公共卫生机构实施,以根据少数急性人类病例改善基于虫媒疾病的地理风险估计。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2bf/3154328/7a0daf066651/pone.0023280.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2bf/3154328/b2aa3f3b790b/pone.0023280.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2bf/3154328/a6b95fe2a72c/pone.0023280.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2bf/3154328/0cf1819462b0/pone.0023280.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2bf/3154328/7a0daf066651/pone.0023280.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2bf/3154328/b2aa3f3b790b/pone.0023280.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2bf/3154328/a6b95fe2a72c/pone.0023280.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2bf/3154328/0cf1819462b0/pone.0023280.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2bf/3154328/7a0daf066651/pone.0023280.g004.jpg

相似文献

1
Predictive mapping of human risk for West Nile virus (WNV) based on environmental and socioeconomic factors.基于环境和社会经济因素的人类西尼罗河病毒(WNV)风险预测图。
PLoS One. 2011;6(8):e23280. doi: 10.1371/journal.pone.0023280. Epub 2011 Aug 10.
2
Factors affecting the geographic distribution of West Nile virus in Georgia, USA: 2002-2004.影响美国佐治亚州西尼罗河病毒地理分布的因素:2002 - 2004年
Vector Borne Zoonotic Dis. 2006 Spring;6(1):73-82. doi: 10.1089/vbz.2006.6.73.
3
A comparison of West Nile virus surveillance using survival analyses of dead corvid and mosquito pool data in Ontario, 2002-2008.2002年至2008年安大略省利用死亡鸦科鸟类和蚊虫样本数据的生存分析对西尼罗河病毒监测进行的比较。
Prev Vet Med. 2015 Dec 1;122(3):363-70. doi: 10.1016/j.prevetmed.2015.10.007. Epub 2015 Oct 19.
4
Rapid GIS-based profiling of West Nile virus transmission: defining environmental factors associated with an urban-suburban outbreak in Northeast Ohio, USA.基于地理信息系统的西尼罗河病毒传播快速概况分析:确定与美国俄亥俄州东北部城市-郊区疫情相关的环境因素。
Geospat Health. 2008 May;2(2):215-25. doi: 10.4081/gh.2008.245.
5
Avian GIS models signal human risk for West Nile virus in Mississippi.鸟类地理信息系统模型显示密西西比州存在西尼罗河病毒的人类感染风险。
Int J Health Geogr. 2006 Aug 31;5:36. doi: 10.1186/1476-072X-5-36.
6
Development and validation of a climate-based ensemble prediction model for West Nile Virus infection rates in Culex mosquitoes, Suffolk County, New York.基于气候的西尼罗河病毒感染率在库蚊中的集合预测模型的开发和验证。纽约州萨福克县
Parasit Vectors. 2016 Aug 9;9(1):443. doi: 10.1186/s13071-016-1720-1.
7
Dynamics of data availability in disease modeling: An example evaluating the trade-offs of ultra-fine-scale factors applied to human West Nile virus disease models in the Chicago area, USA.疾病建模中数据可用性的动态变化:以美国芝加哥地区人类西尼罗河病毒疾病模型为例,评估超精细尺度因素的权衡取舍。
PLoS One. 2021 May 19;16(5):e0251517. doi: 10.1371/journal.pone.0251517. eCollection 2021.
8
Vector surveillance for West Nile virus.西尼罗河病毒的病媒监测
Ann N Y Acad Sci. 2001 Dec;951:74-83. doi: 10.1111/j.1749-6632.2001.tb02686.x.
9
Reducing West Nile Virus Risk Through Vector Management.通过病媒管理降低西尼罗河病毒风险。
J Med Entomol. 2019 Oct 28;56(6):1516-1521. doi: 10.1093/jme/tjz083.
10
Factors That Influence the Transmission of West Nile Virus in Florida.影响西尼罗河病毒在佛罗里达州传播的因素。
J Med Entomol. 2015 Sep;52(5):743-54. doi: 10.1093/jme/tjv076. Epub 2015 Jul 1.

引用本文的文献

1
The impact of climate change on transmission season length: West Nile virus as a case study.气候变化对传播季节长度的影响:以西尼罗河病毒为例的研究
bioRxiv. 2025 Aug 2:2025.08.01.667982. doi: 10.1101/2025.08.01.667982.
2
Impacts of Urbanization and Habitat Characteristics on the Human Risk of West Nile Disease in the United States.城市化和栖息地特征对美国西尼罗河疾病人类风险的影响。
Biology (Basel). 2025 Feb 20;14(3):224. doi: 10.3390/biology14030224.
3
Regional variation in the landscape ecology of West Nile virus sentinel chicken seroconversion in Florida.

本文引用的文献

1
Economic conditions predict prevalence of West Nile virus.经济状况可预测西尼罗河病毒的流行情况。
PLoS One. 2010 Nov 12;5(11):e15437. doi: 10.1371/journal.pone.0015437.
2
Disentangling the effect of local and global spatial variation on a mosquito-borne infection in a neotropical heterogeneous environment.厘清局域和全域空间变异性对新热带地区异质环境中蚊媒传染病的影响。
Am J Trop Med Hyg. 2010 Feb;82(2):194-201. doi: 10.4269/ajtmh.2010.09-0040.
3
Risk factors for human infection with West Nile Virus in Connecticut: a multi-year analysis.
佛罗里达州西尼罗河病毒哨鸡血清转换景观生态学的地域差异。
PLoS One. 2024 Oct 25;19(10):e0305510. doi: 10.1371/journal.pone.0305510. eCollection 2024.
4
Ecological Niche and Positive Clusters of Two West Nile Virus Vectors in Ontario, Canada.加拿大安大略省两种西尼罗河病毒载体的生态位和正聚类。
Ecohealth. 2023 Sep;20(3):249-262. doi: 10.1007/s10393-023-01653-8. Epub 2023 Nov 20.
5
Mapping the abundance of endemic mosquito-borne diseases vectors in southern Quebec.魁北克省南部地方性蚊媒传染病媒介丰度的绘图。
BMC Public Health. 2023 May 22;23(1):924. doi: 10.1186/s12889-023-15773-x.
6
Artificial intelligence to predict West Nile virus outbreaks with eco-climatic drivers.利用生态气候驱动因素通过人工智能预测西尼罗河病毒爆发。
Lancet Reg Health Eur. 2022 Mar 30;17:100370. doi: 10.1016/j.lanepe.2022.100370. eCollection 2022 Jun.
7
Predicting eastern equine encephalitis spread in North America: An ecological study.预测东部马脑炎在北美的传播:一项生态学研究。
Curr Res Parasitol Vector Borne Dis. 2021 Nov 26;1:100064. doi: 10.1016/j.crpvbd.2021.100064. eCollection 2021.
8
Spatiotemporal Analysis of West Nile Virus Epidemic in South Banat District, Serbia, 2017-2019.2017 - 2019年塞尔维亚南巴纳特地区西尼罗河病毒疫情的时空分析
Animals (Basel). 2021 Oct 13;11(10):2951. doi: 10.3390/ani11102951.
9
A proposed framework for the development and qualitative evaluation of West Nile virus models and their application to local public health decision-making.一个西尼罗河病毒模型的开发和定性评估以及其在当地公共卫生决策中的应用的建议框架。
PLoS Negl Trop Dis. 2021 Sep 9;15(9):e0009653. doi: 10.1371/journal.pntd.0009653. eCollection 2021 Sep.
10
Arboviral diseases and poverty in Alabama, 2007-2017.阿拉巴马州 2007-2017 年虫媒病毒病与贫困。
PLoS Negl Trop Dis. 2021 Jul 6;15(7):e0009535. doi: 10.1371/journal.pntd.0009535. eCollection 2021 Jul.
康涅狄格州西尼罗河病毒感染人体的危险因素:一项多年分析。
Int J Health Geogr. 2009 Nov 27;8:67. doi: 10.1186/1476-072X-8-67.
4
Landscape predictors of tick-borne encephalitis in Latvia: land cover, land use, and land ownership.拉脱维亚 tick-borne encephalitis 的景观预测因子:土地覆盖、土地利用和土地所有权。
Vector Borne Zoonotic Dis. 2010 Jun;10(5):497-506. doi: 10.1089/vbz.2009.0116.
5
Landscape epidemiology of vector-borne diseases.虫媒传染病的景观流行病学。
Annu Rev Entomol. 2010;55:461-83. doi: 10.1146/annurev-ento-112408-085419.
6
Spatially explicit West Nile virus risk modeling in Santa Clara County, California.加利福尼亚州圣克拉拉县西尼罗河病毒风险的空间明确建模
Vector Borne Zoonotic Dis. 2009 Jun;9(3):267-74. doi: 10.1089/vbz.2008.0084.
7
Distribution and abundance of host-seeking Culex species at three proximate locations with different levels of West Nile virus activity.在西尼罗河病毒活动水平不同的三个相邻地点,宿主寻找型库蚊种类的分布与丰度。
Am J Trop Med Hyg. 2009 Apr;80(4):661-8.
8
An examination of the effect of landscape pattern, land surface temperature, and socioeconomic conditions on WNV dissemination in Chicago.探讨景观格局、地表温度和社会经济条件对芝加哥西尼罗河病毒传播的影响。
Environ Monit Assess. 2009 Dec;159(1-4):143-61. doi: 10.1007/s10661-008-0618-6. Epub 2008 Dec 24.
9
Spatio-temporal analysis of the relationship between WNV dissemination and environmental variables in Indianapolis, USA.美国印第安纳波利斯西尼罗河病毒传播与环境变量之间关系的时空分析
Int J Health Geogr. 2008 Dec 18;7:66. doi: 10.1186/1476-072X-7-66.
10
Disease emergence from global climate and land use change.全球气候和土地利用变化导致的疾病出现。
Med Clin North Am. 2008 Nov;92(6):1473-91, xii. doi: 10.1016/j.mcna.2008.07.007.