AI-assisted tracking of worldwide non-pharmaceutical interventions for COVID-19.
AI-assisted tracking of worldwide non-pharmaceutical interventions for COVID-19.
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DOI：
10.1038/s41597-021-00878-y
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发表时间：
2021-03-25
期刊：
影响因子：
9.8
通讯作者：
Rosen-Zvi M
中科院分区：
文献类型：
--
作者：
Suryanarayanan P;Tsou CH;Poddar A;Mahajan D;Dandala B;Madan P;Agrawal A;Wachira C;Samuel OM;Bar-Shira O;Kipchirchir C;Okwako S;Ogallo W;Otieno F;Nyota T;Matu F;Barros VR;Shats D;Kagan O;Remy S;Bent O;Guhan P;Mahatma S;Walcott-Bryant A;Pathak D;Rosen-Zvi M
2019冠状病毒病（COVID-19）全球大流行几乎改变了世界各地人类社会的方方面面。针对一种新出现的高传染性疾病，世界各国政府已经实施了非药物干预措施（NPI），以减缓病毒的传播。这类干预措施的例子包括社区行动，如关闭学校或限制大规模集会，个人行动，包括戴口罩和自我隔离，以及环境行动，如清洁公共设施。我们提出了全球COVID-19非药物干预跟踪（WNTRAC），这是一个全面的数据集，包括自大流行开始以来全球实施的6，000多项NPI。WNTRAC涵盖了在261个国家和地区实施的NPI，并将NPI分为16种NPI类型。NPI每天使用自然语言处理技术从维基百科文章中自动提取，然后手动验证以确保准确性和真实性。我们希望该数据集将证明对政策制定者、公共卫生领导者和研究人员在控制COVID-19传播的建模和分析工作中有价值。描述报告数据的机器可访问元数据文件：10.6084/m9.figshare.13999484
The Coronavirus disease 2019 (COVID-19) global pandemic has transformed almost every facet of human society throughout the world. Against an emerging, highly transmissible disease, governments worldwide have implemented non-pharmaceutical interventions (NPIs) to slow the spread of the virus. Examples of such interventions include community actions, such as school closures or restrictions on mass gatherings, individual actions including mask wearing and self-quarantine, and environmental actions such as cleaning public facilities. We present the Worldwide Non-pharmaceutical Interventions Tracker for COVID-19 (WNTRAC), a comprehensive dataset consisting of over 6,000 NPIs implemented worldwide since the start of the pandemic. WNTRAC covers NPIs implemented across 261 countries and territories, and classifies NPIs into a taxonomy of 16 NPI types. NPIs are automatically extracted daily from Wikipedia articles using natural language processing techniques and then manually validated to ensure accuracy and veracity. We hope that the dataset will prove valuable for policymakers, public health leaders, and researchers in modeling and analysis efforts to control the spread of COVID-19. Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13999484
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影响因子：
9.8
作者：
Suryanarayanan P;Tsou CH;Poddar A;Mahajan D;Dandala B;Madan P;Agrawal A;Wachira C;Samuel OM;Bar-Shira O;Kipchirchir C;Okwako S;Ogallo W;Otieno F;Nyota T;Matu F;Barros VR;Shats D;Kagan O;Remy S;Bent O;Guhan P;Mahatma S;Walcott-Bryant A;Pathak D;Rosen-Zvi M
通讯作者：
Rosen-Zvi M
影响因子：
3.5
作者：
Eubank, S.;Eckstrand, I;Barrett, C. L.
通讯作者：
Barrett, C. L.
影响因子：
29.9
作者：
Cheng, Cindy;Barcelo, Joan;Messerschmidt, Luca
通讯作者：
Messerschmidt, Luca
影响因子：
9.8
作者：
Desvars-Larrive, Amelie;Dervic, Elma;Thurner, Stefan
通讯作者：
Thurner, Stefan