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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DOI：
10.1080/20964471.2020.1844934
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发表时间：
2021-01-01
期刊：
影响因子：
4
通讯作者：
Yang, Chaowei
中科院分区：
文献类型：
--
作者：
Sha, Dexuan;Liu, Yi;Yang, Chaowei
在新冠肺炎这场全球卫生危机下，及时、准确的疫情数据对于观察、监测、分析、建模、预测和减轻影响至关重要。病毒病例数据可以在大流行的背景下与各种应用的相关因素联合分析。当前的新冠肺炎案件数据分散在各种数据源，数据质量不高，数据结构不一致。针对这一不足，提出了一种基于时空立方体的多尺度时空数据产品作为公共仓库平台，通过采用不同的数据标准来集成不同的数据源。在时空立方体内，全面的数据处理工作流收集全球、国家、省/州、县和市级别的不同的新冠肺炎疫情数据集。这一拟议的框架得到了2小时频率的自动更新和众包验证小组的支持，以产生和更新每日时间步长的数据。这一快速反应数据集允许将其他相关的社会经济和环境因素结合起来进行时空分析。这些数据可以在哈佛数据中心平台(https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/8HGECN)和GitHub开放源码库(https://github.com/stccenter/COVID-19-Data).)中获得
Under the global health crisis of COVID-19, timely, and accurate epidemic 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 shortcoming, 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 datasets 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 environmental factors for spatiotemporal analysis. The data is available in Harvard Dataverse platform (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/8HGECN) and GitHub open source repository (https://github.com/stccenter/COVID-19-Data).