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COVID-19 Open-Data a global-scale spatially granular meta-dataset for coronavirus disease.
Wahltinez, Oscar; Cheung, Aurora; Alcantara, Ruth; Cheung, Donny; Daswani, Mayank; Erlinger, Anthony; Lee, Matt; Yawalkar, Pranali; Lê, Paula; Navarro, Ofir Picazo; Brenner, Michael P; Murphy, Kevin.
Afiliação
  • Wahltinez O; Google, Mountain View, California, USA. owahltinez@google.com.
  • Cheung A; Department of Artificial Intelligence, Universidad Nacional de Educacion a Distancia (UNED), Madrid, Spain. owahltinez@google.com.
  • Alcantara R; Google, Mountain View, California, USA.
  • Cheung D; Google, Mountain View, California, USA.
  • Daswani M; Google, Mountain View, California, USA.
  • Erlinger A; Google, Mountain View, California, USA.
  • Lee M; Google, Mountain View, California, USA.
  • Yawalkar P; Google, Mountain View, California, USA.
  • Lê P; Google, Mountain View, California, USA.
  • Navarro OP; Google, Mountain View, California, USA.
  • Brenner MP; Google, Mountain View, California, USA.
  • Murphy K; Google, Mountain View, California, USA.
Sci Data ; 9(1): 162, 2022 04 12.
Article em En | MEDLINE | ID: mdl-35413965
ABSTRACT
This paper introduces the COVID-19 Open Dataset (COD), available at goo.gle/covid-19-open-data . A static copy is of the dataset is also available at https//doi.org/10.6084/m9.figshare.c.5399355 . This is a very large "meta-dataset" of COVID-related data, containing epidemiological information, from 22,579 unique locations within 232 different countries and independent territories. For 62 of these countries we have state-level data, and for 23 of these countries we have county-level data. For 15 countries, COD includes cases and deaths stratified by age or sex. COD also contains information on hospitalizations, vaccinations, and other relevant factors such as mobility, non-pharmaceutical interventions and static demographic attributes. Each location is tagged with a unique identifier so that these different types of information can be easily combined. The data is automatically extracted from 121 different authoritative sources, using scalable open source software. This paper describes the format and construction of the dataset, and includes a preliminary statistical analysis of its content, revealing some interesting patterns.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: SARS-CoV-2 / COVID-19 Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Sci Data Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: SARS-CoV-2 / COVID-19 Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Sci Data Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos