• 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

迈向基于半自动化的虫媒传染病预警系统。

Towards a Semi-Automatic Early Warning System for Vector-Borne Diseases.

机构信息

Bioapplications Ltd., 30 Ioannou Perganta Str., 32100 Levadia, Greece.

Laboratory of Agricultural Zoology and Entomology, Department of Crop Science, Agricultural University of Athens, 75 Iera Odos Str., 11855 Athens, Greece.

出版信息

Int J Environ Res Public Health. 2021 Feb 13;18(4):1823. doi: 10.3390/ijerph18041823.

DOI:10.3390/ijerph18041823
PMID:33668472
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7918487/
Abstract

The emergence and spread of vector-borne diseases (VBDs) is a function of biotic, abiotic and socio-economic drivers of disease while their economic and societal burden depends upon a number of time-varying factors. This work is concerned with the development of an early warning system that can act as a predictive tool for public health preparedness and response. We employ a host-vector model that combines entomological (mosquito data), social (immigration rate, demographic data), environmental (temperature) and geographical data (risk areas). The output consists of appropriate maps depicting suitable risk measures such as the basic reproduction number, , and the probability of getting infected by the disease. These tools consist of the backbone of a semi-automatic early warning system tool which can potentially aid the monitoring and control of VBDs in different settings. In addition, it can be used for optimizing the cost-effectiveness of distinct control measures and the integration of open geospatial and climatological data. The R code used to generate the risk indicators and the corresponding spatial maps along with the data is made available.

摘要

虫媒传染病（VBD）的出现和传播是疾病的生物、非生物和社会经济驱动因素的函数，而其经济和社会负担取决于许多时变因素。这项工作涉及开发一种预警系统，该系统可以作为公共卫生准备和应对的预测工具。我们采用了一种宿主-媒介模型，该模型结合了昆虫学（蚊子数据）、社会（移民率、人口数据）、环境（温度）和地理数据（风险区域）。输出包括适当的地图，描绘了适当的风险指标，如基本繁殖数 ，和感染疾病的概率。这些工具构成了半自动预警系统工具的骨干，该工具可潜在地帮助监测和控制不同环境下的 VBD。此外，它可用于优化不同控制措施的成本效益，并整合开放的地理空间和气候数据。生成风险指标和相应空间地图的 R 代码以及相关数据都可提供。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/6021c0399f95/ijerph-18-01823-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/22d5611187a2/ijerph-18-01823-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/58f300cde0d5/ijerph-18-01823-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/bcb4f8bf13e7/ijerph-18-01823-g0A3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/3d2aada88171/ijerph-18-01823-g0A4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/da6745aef0ae/ijerph-18-01823-g0A5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/15bf66027117/ijerph-18-01823-g0A6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/89023964e627/ijerph-18-01823-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/9277070a5af9/ijerph-18-01823-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/0c5588665376/ijerph-18-01823-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/d3b9b40d4379/ijerph-18-01823-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/6021c0399f95/ijerph-18-01823-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/22d5611187a2/ijerph-18-01823-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/58f300cde0d5/ijerph-18-01823-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/bcb4f8bf13e7/ijerph-18-01823-g0A3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/3d2aada88171/ijerph-18-01823-g0A4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/da6745aef0ae/ijerph-18-01823-g0A5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/15bf66027117/ijerph-18-01823-g0A6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/89023964e627/ijerph-18-01823-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/9277070a5af9/ijerph-18-01823-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/0c5588665376/ijerph-18-01823-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/d3b9b40d4379/ijerph-18-01823-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c087/7918487/6021c0399f95/ijerph-18-01823-g005.jpg

相似文献

1
Towards a Semi-Automatic Early Warning System for Vector-Borne Diseases.迈向基于半自动化的虫媒传染病预警系统。
Int J Environ Res Public Health. 2021 Feb 13;18(4):1823. doi: 10.3390/ijerph18041823.
2
Inter-annual variability of the effects of intrinsic and extrinsic drivers affecting West Nile virus vector Culex pipiens population dynamics in northeastern Italy.影响意大利东北部西尼罗河病毒载体库蚊种群动态的内在和外在驱动因素的年际变化。
Parasit Vectors. 2020 May 29;13(1):271. doi: 10.1186/s13071-020-04143-w.
3
The influence of vector-borne disease on human history: socio-ecological mechanisms.虫媒传染病对人类历史的影响：社会生态机制。
Ecol Lett. 2021 Apr;24(4):829-846. doi: 10.1111/ele.13675. Epub 2021 Jan 27.
4
A spatial predictive model for malaria resurgence in central Greece integrating entomological, environmental and social data.整合昆虫学、环境和社会数据的希腊中部疟疾复发空间预测模型。
PLoS One. 2017 Jun 29;12(6):e0178836. doi: 10.1371/journal.pone.0178836. eCollection 2017.
5
Deriving risk maps from epidemiological models of vector borne diseases: State-of-the-art and suggestions for best practice.从虫媒传染病的流行病学模型中得出风险图：最新技术和最佳实践建议。
Epidemics. 2020 Dec;33:100411. doi: 10.1016/j.epidem.2020.100411. Epub 2020 Oct 22.
6
A Population Dynamics Model of Mosquito-Borne Disease Transmission, Focusing on Mosquitoes' Biased Distribution and Mosquito Repellent Use.基于蚊虫偏置分布和驱蚊剂使用的蚊媒疾病传播的种群动力学模型
Bull Math Biol. 2019 Dec;81(12):4977-5008. doi: 10.1007/s11538-019-00666-1. Epub 2019 Oct 8.
7
Impact of venereal transmission on the dynamics of vertically transmitted viral diseases among mosquitoes.性病传播对蚊子垂直传播病毒病动态的影响。
Math Biosci. 2020 Jul;325:108366. doi: 10.1016/j.mbs.2020.108366. Epub 2020 May 5.
8
[Current malaria situation in the Republic of Kazakhstan].[哈萨克斯坦共和国当前的疟疾形势]
Med Parazitol (Mosk). 2001 Jan-Mar(1):24-33.
9
Vector-Borne Disease Models with Active and Inactive Vectors: A Simple Way to Consider Biting Behavior.具有活跃和不活跃媒介的虫媒疾病模型：一种考虑叮咬行为的简单方法。
Bull Math Biol. 2021 Dec 23;84(1):22. doi: 10.1007/s11538-021-00972-7.
10
Estimating the temporal and spatial risk of bluetongue related to the incursion of infected vectors into Switzerland.评估与受感染媒介侵入瑞士相关的蓝舌病的时空风险。
BMC Vet Res. 2008 Oct 15;4:42. doi: 10.1186/1746-6148-4-42.

本文引用的文献

1
Population dynamics, pathogen detection and insecticide resistance of mosquito and sand fly in refugee camps, Greece.难民收容所中蚊子和沙蝇的种群动态、病原体检测和杀虫剂抗性。
Infect Dis Poverty. 2020 Mar 18;9(1):30. doi: 10.1186/s40249-020-0635-4.
2
Can data from native mosquitoes support determining invasive species habitats? Modelling the climatic niche of Aedes japonicus japonicus (Diptera, Culicidae) in Germany.本土蚊子的数据能否支持确定入侵物种的栖息地？日本伊蚊在德国的气候生态位建模（双翅目，蚊科）。
Parasitol Res. 2020 Jan;119(1):31-42. doi: 10.1007/s00436-019-06513-5. Epub 2019 Nov 26.
3
Thermal biology of mosquito-borne disease.
蚊媒疾病的热生物学
Ecol Lett. 2019 Oct;22(10):1690-1708. doi: 10.1111/ele.13335. Epub 2019 Jul 8.
4
Elucidating fitness components of the invasive dermestid beetle Trogoderma granarium combining deterministic and stochastic demography.结合确定性和随机性人口统计学阐明入侵性皮蠹甲虫 Trogoderma granarium 的适合度成分。
PLoS One. 2019 Feb 14;14(2):e0212182. doi: 10.1371/journal.pone.0212182. eCollection 2019.
5
Fresh-blood-free diet for rearing malaria mosquito vectors.无新鲜血饲料饲养疟蚊媒介。
Sci Rep. 2018 Dec 13;8(1):17807. doi: 10.1038/s41598-018-35886-3.
6
Numerical modeling of mosquito population dynamics of Aedes aegypti.登革热媒介伊蚊种群动态的数值建模。
Parasit Vectors. 2018 Apr 16;11(1):245. doi: 10.1186/s13071-018-2829-1.
7
Addressing vulnerability, building resilience: community-based adaptation to vector-borne diseases in the context of global change.应对脆弱性，增强复原力：在全球变化背景下基于社区的病媒传播疾病适应。
Infect Dis Poverty. 2017 Dec 11;6(1):166. doi: 10.1186/s40249-017-0375-2.
8
A spatial predictive model for malaria resurgence in central Greece integrating entomological, environmental and social data.整合昆虫学、环境和社会数据的希腊中部疟疾复发空间预测模型。
PLoS One. 2017 Jun 29;12(6):e0178836. doi: 10.1371/journal.pone.0178836. eCollection 2017.
9
Prototype early warning systems for vector-borne diseases in Europe.欧洲虫媒疾病早期预警系统原型
Int J Environ Res Public Health. 2015 Jun 2;12(6):6333-51. doi: 10.3390/ijerph120606333.
10
Modelling the spatial distribution of the nuisance mosquito species Anopheles plumbeus (Diptera: Culicidae) in the Netherlands.模拟荷兰有害蚊虫物种铅色按蚊（双翅目：蚊科）的空间分布。
Parasit Vectors. 2015 May 1;8:258. doi: 10.1186/s13071-015-0865-7.