首页|A spatiotemporal data collection of viral cases for COVID-19 rapid response

A spatiotemporal data collection of viral cases for COVID-19 rapid response

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  • NETL
  • NSTL
  • 万方数据
  • 维普
Under the global health crisis of COVID-19,timely,and accurate epi-demic data are important for observation,monitoring,analyzing,modeling,predicting,and mitigating impacts.Viral case data can be jointly analyzed with relevant factors for various applications in the context of the pandemic Current COVID-19 case data are scattered across a variety of data sources which may consist of low data quality accompanied by inconsistent data structures.To address this short-coming,a multi-scale spatiotemporal data product is proposed as a public repository platform,based on a spatiotemporal cube,and allows the integration of different data sources by adopting various data standards.Within the spatiotemporal cube,a comprehensive data processing workflow gathers disparate COVID-19 epidemic data-sets at the global,national,provincial/state,county,and city levels.This proposed framework is supported by an automatic update with a 2-h frequency and the crowdsourcing validation team to produce and update data on a daily time step.This rapid-response dataset allows the integration of other relevant socio-economic and environ-mental factors for spatiotemporal analysis.The data is available in Harvard Dataverse platform(https://dataverse.harvard.edu/dataset.xhtml?persistentld=doi:10.7910/DVN/8HGECN)and GitHub open source repository(https://github.com/stccenter/COVID-19-Data).

COVID-19 pandemicpublic healthsemi-automatic validationspatiotemporal data set

Dexuan Sha、Yi Liu、Qian Liu、Yun Li、Yifei Tian、Fayez Beaini、Cheng Zhong、Tao Hu、Zifu Wang、Hai Lan、You Zhou、Zhiran Zhang、Chaowei Yang

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NSF Spatiotemporal Innovation Center,George Mason University,Fairfax,VA,USA

Department of Geography and GeoInformation Science,George Mason University,Fairfax,VA,USA

Department of Aerospace and Mechanical Engineering,University of Notre Dame,Notre Dame,IN,USA

Center for Geographic Analysis,Harvard University,Cambridge,MA,USA

School of Resource and Environmental Sciences,Wuhan University,Wuhan,Hubei,China

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18415201835507

2021

地球大数据(英文版)

地球大数据(英文版)

ISSN：
年,卷(期)：2021.5(1)
  • 68