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Mobility, exposure, and epidemiological timelines of COVID-19 infections in China outside Hubei province.
Liu, Xiao Fan; Xu, Xiao-Ke; Wu, Ye.
  • Liu XF; Web Mining Laboratory, Department of Media and Communication, City University of Hong Kong, Hong Kong Special Administrative Region, China.
  • Xu XK; College of Information and Communication Engineering, Dalian Minzu University, Dalian, 116600, China. xuxiaoke@foxmail.com.
  • Wu Y; Computational Communication Research Center, Beijing Normal University, Zhuhai, 519087, China. wuye@bnu.edu.cn.
Sci Data ; 8(1): 54, 2021 02 05.
Article in English | MEDLINE | ID: covidwho-1065923
ABSTRACT
The 2019 coronavirus disease (COVID-19) is pseudonymously linked to more than 100 million cases in the world as of January 2021. High-quality data are needed but lacking in the understanding of and fighting against COVID-19. We provide a complete and updating hand-coded line-list dataset containing detailed information of the cases in China and outside the epicenter in Hubei province. The data are extracted from public disclosures by local health authorities, starting from January 19. This dataset contains a very rich set of features for the characterization of COVID-19's epidemiological properties, including individual cases' demographic information, travel history, potential virus exposure scenario, contacts with known infections, and timelines of symptom onset, quarantine, infection confirmation, and hospitalization. These cases can be considered the baseline COVID-19 transmissibility under extreme mitigation measures, and therefore, a reference for comparative scientific investigation and public policymaking.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study Limits: Humans Country/Region as subject: Asia Language: English Journal: Sci Data Year: 2021 Document Type: Article Affiliation country: S41597-021-00844-8

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study Limits: Humans Country/Region as subject: Asia Language: English Journal: Sci Data Year: 2021 Document Type: Article Affiliation country: S41597-021-00844-8