• 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

空间生命历程流行病学与传染病研究。

Spatial Lifecourse Epidemiology and Infectious Disease Research.

机构信息

School of Resources and Environmental Science, Wuhan University, Wuhan, China; Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China; International Initiative on Spatial Lifecourse Epidemiology (ISLE), Hong Kong, China.

State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing, 100875, China; Faculty of Geography, Beijing Normal University, Beijing, 100875, China.

出版信息

Trends Parasitol. 2020 Mar;36(3):235-238. doi: 10.1016/j.pt.2019.12.012. Epub 2020 Feb 7.

DOI:10.1016/j.pt.2019.12.012
PMID:32044243
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7172117/
Abstract

Spatial lifecourse epidemiology aims to utilize advanced spatial, location-aware, and artificial intelligence technologies to investigate long-term effects of measurable biological, environmental, behavioral, and psychosocial factors on individual risk for chronic diseases. It could also further the research on infectious disease dynamics, risks, and consequences across the life course.

摘要

空间生命历程流行病学旨在利用先进的空间、位置感知和人工智能技术,研究可测量的生物、环境、行为和社会心理因素对个体慢性病风险的长期影响。它还可以进一步研究传染病在生命历程中的动态、风险和后果。

相似文献

1
Spatial Lifecourse Epidemiology and Infectious Disease Research.空间生命历程流行病学与传染病研究。
Trends Parasitol. 2020 Mar;36(3):235-238. doi: 10.1016/j.pt.2019.12.012. Epub 2020 Feb 7.
2
[Spatial lifecourse epidemiology in chronic non-communicable disease research].[慢性非传染性疾病研究中的空间生命历程流行病学]
Zhonghua Liu Xing Bing Xue Za Zhi. 2022 May 10;43(5):755-760. doi: 10.3760/cma.j.cn112338-20220108-00013.
3
Spatial Lifecourse Epidemiology Reporting Standards (ISLE-ReSt) statement.空间生命历程流行病学报告标准(ISLE-ReSt)声明。
Health Place. 2020 Jan;61:102243. doi: 10.1016/j.healthplace.2019.102243. Epub 2019 Dec 4.
4
Progress and Challenges in Infectious Disease Cartography.传染病制图的进展与挑战
Trends Parasitol. 2016 Jan;32(1):19-29. doi: 10.1016/j.pt.2015.09.006. Epub 2015 Oct 23.
5
[The specific epidemiological features of infectious and noninfectious diseases].[传染病和非传染病的具体流行病学特征]
Ter Arkh. 1994;66(11):4-6.
6
Infectious disease epidemiology in the 21st century: will it be eradicated or will it reemerge?21世纪的传染病流行病学:它会被根除还是会再次出现?
Epidemiol Rev. 2000;22(1):57-63. doi: 10.1093/oxfordjournals.epirev.a018024.
7
Spatiobehavioral Characteristics - Defining the Epidemiology of New Contagious Diseases at the Earliest Moment Possible.时空行为特征——在最早时刻定义新发传染病的流行病学。
Trends Parasitol. 2021 Mar;37(3):179-181. doi: 10.1016/j.pt.2020.12.004. Epub 2021 Jan 21.
8
The incubation period and the dynamics of infectious disease.传染病的潜伏期及动态变化
Am J Epidemiol. 1966 Mar;83(2):204-6. doi: 10.1093/oxfordjournals.aje.a120576.
9
Life course epidemiology and infectious diseases.生命历程流行病学与传染病
Int J Epidemiol. 2002 Apr;31(2):300-1.
10
[Basic future research trends in the general epidemiology and prevention of infectious diseases].[传染病一般流行病学与预防的基础未来研究趋势]
Zh Mikrobiol Epidemiol Immunobiol. 1981 Jun(6):3-7.

引用本文的文献

1
Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis.用于传染病监测、诊断和预后的机器学习与人工智能
Viruses. 2025 Jun 23;17(7):882. doi: 10.3390/v17070882.
2
Editorial: Spatial epidemiology.社论:空间流行病学
Front Public Health. 2025 Jan 24;12:1522631. doi: 10.3389/fpubh.2024.1522631. eCollection 2024.
3
Spatiotemporal distribution of schistosomiasis transmission risk in Jiangling County, Hubei Province, P.R. China.中国湖北省江陵县血吸虫病传播风险的时空分布。

本文引用的文献

1
Spatial Lifecourse Epidemiology Reporting Standards (ISLE-ReSt) statement.空间生命历程流行病学报告标准(ISLE-ReSt)声明。
Health Place. 2020 Jan;61:102243. doi: 10.1016/j.healthplace.2019.102243. Epub 2019 Dec 4.
2
Top 10 Research Priorities in Spatial Lifecourse Epidemiology.空间生命历程流行病学十大研究重点。
Environ Health Perspect. 2019 Jul;127(7):74501. doi: 10.1289/EHP4868. Epub 2019 Jul 4.
3
Identifying influential spreaders in complex networks by propagation probability dynamics.基于传播概率动态识别复杂网络中有影响力的传播者。
PLoS Negl Trop Dis. 2023 May 4;17(5):e0011265. doi: 10.1371/journal.pntd.0011265. eCollection 2023 May.
4
Artificial intelligence against the first wave of COVID-19: evidence from China.人工智能对抗 COVID-19 第一波疫情:来自中国的证据。
BMC Health Serv Res. 2022 Jun 10;22(1):767. doi: 10.1186/s12913-022-08146-4.
5
HIV-1 genetic transmission networks among people living with HIV/AIDS in Sichuan, China: a genomic and spatial epidemiological analysis.中国四川艾滋病病毒/艾滋病感染者中的HIV-1基因传播网络:一项基因组与空间流行病学分析
Lancet Reg Health West Pac. 2021 Nov 23;18:100318. doi: 10.1016/j.lanwpc.2021.100318. eCollection 2022 Jan.
6
Scenario prediction of public health emergencies using infectious disease dynamics model and dynamic Bayes.基于传染病动力学模型和动态贝叶斯的突发公共卫生事件情景预测
Future Gener Comput Syst. 2022 Feb;127:334-346. doi: 10.1016/j.future.2021.09.028. Epub 2021 Sep 21.
7
Cross-disciplinary approaches to assist with nucleic acid testing for SARS-CoV-2.跨学科方法辅助 SARS-CoV-2 的核酸检测。
Appl Microbiol Biotechnol. 2021 Aug;105(16-17):6291-6299. doi: 10.1007/s00253-021-11498-2. Epub 2021 Aug 23.
8
Spatial technologies to strengthen traditional testing for SARS-CoV-2.利用空间技术加强对 SARS-CoV-2 的传统检测。
Trends Microbiol. 2021 Dec;29(12):1055-1057. doi: 10.1016/j.tim.2021.03.003. Epub 2021 Mar 14.
9
The Impact of COVID-19 Management Policies Tailored to Airborne SARS-CoV-2 Transmission: Policy Analysis.《基于 SARS-CoV-2 空气传播的新冠管理政策的影响:政策分析》。
JMIR Public Health Surveill. 2021 Apr 21;7(4):e20699. doi: 10.2196/20699.
10
An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China.扩展权重核密度估计模型预测 COVID-19 发病风险,并识别中国封锁效应的时空变化。
Commun Biol. 2021 Jan 25;4(1):126. doi: 10.1038/s42003-021-01677-2.
Chaos. 2019 Mar;29(3):033120. doi: 10.1063/1.5055069.
4
Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine.测量移动性、疾病关联性和个体风险:利用移动电话数据和移动医疗进行旅行医学研究的综述。
J Travel Med. 2019 May 10;26(3). doi: 10.1093/jtm/taz019.
5
Surveillance and characterisation of influenza viruses among patients with influenza-like illness in Bali, Indonesia, July 2010-June 2014.2010 年 7 月至 2014 年 6 月期间,印度尼西亚巴厘岛流感样疾病患者中流感病毒的监测和特征描述。
BMC Infect Dis. 2019 Mar 7;19(1):231. doi: 10.1186/s12879-019-3842-5.
6
Climate-driven variation in mosquito density predicts the spatiotemporal dynamics of dengue.气候驱动的蚊子密度变化预测登革热的时空动态。
Proc Natl Acad Sci U S A. 2019 Feb 26;116(9):3624-3629. doi: 10.1073/pnas.1806094116. Epub 2019 Feb 11.
7
Spatial lifecourse epidemiology.空间生命历程流行病学
Lancet Planet Health. 2019 Feb;3(2):e57-e59. doi: 10.1016/S2542-5196(18)30245-6.
8
Earth Observation: Investigating Noncommunicable Diseases from Space.地球观测:从太空研究非传染性疾病。
Annu Rev Public Health. 2019 Apr 1;40:85-104. doi: 10.1146/annurev-publhealth-040218-043807. Epub 2019 Jan 11.
9
Seasonal and interannual risks of dengue introduction from South-East Asia into China, 2005-2015.2005-2015 年东南亚输入中国登革热的季节性和年际风险。
PLoS Negl Trop Dis. 2018 Nov 9;12(11):e0006743. doi: 10.1371/journal.pntd.0006743. eCollection 2018 Nov.
10
Intensity of exposure to pulmonary tuberculosis determines risk of tuberculosis infection and disease.接触肺结核的强度决定了感染和发病的风险。
Eur Respir J. 2018 Jan 18;51(1). doi: 10.1183/13993003.01578-2017. Print 2018 Jan.