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A worldwide epidemiological database for COVID-19 at fine-grained spatial resolution
This database provides the daily time-series of COVID-19 cases, deaths, recovered people, tests, vaccinations, and hospitalizations, for more than 230 countries, 760 regions, and 12,000 lower-level administrative divisions. The geographical entities are associated with identifiers to match with hydr...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8964767/ https://www.ncbi.nlm.nih.gov/pubmed/35351921 http://dx.doi.org/10.1038/s41597-022-01245-1 |
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author | Guidotti, Emanuele |
author_facet | Guidotti, Emanuele |
author_sort | Guidotti, Emanuele |
collection | PubMed |
description | This database provides the daily time-series of COVID-19 cases, deaths, recovered people, tests, vaccinations, and hospitalizations, for more than 230 countries, 760 regions, and 12,000 lower-level administrative divisions. The geographical entities are associated with identifiers to match with hydrometeorological, geospatial, and mobility data. The database includes policy measures at the national and, when available, sub-national levels. The data acquisition pipeline is open-source and fully automated. As most governments revise the data retrospectively, the database always updates the complete time-series to mirror the original source. Vintage data, immutable snapshots of the data taken each day, are provided to ensure research reproducibility. The latest data are updated on an hourly basis, and the vintage data are available since April 14, 2020. All the data are available in CSV files or SQLite format. By unifying the access to the data, this work makes it possible to study the pandemic on a global scale with high resolution, taking into account within-country variations, nonpharmaceutical interventions, and environmental and exogenous variables. |
format | Online Article Text |
id | pubmed-8964767 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-89647672022-04-12 A worldwide epidemiological database for COVID-19 at fine-grained spatial resolution Guidotti, Emanuele Sci Data Data Descriptor This database provides the daily time-series of COVID-19 cases, deaths, recovered people, tests, vaccinations, and hospitalizations, for more than 230 countries, 760 regions, and 12,000 lower-level administrative divisions. The geographical entities are associated with identifiers to match with hydrometeorological, geospatial, and mobility data. The database includes policy measures at the national and, when available, sub-national levels. The data acquisition pipeline is open-source and fully automated. As most governments revise the data retrospectively, the database always updates the complete time-series to mirror the original source. Vintage data, immutable snapshots of the data taken each day, are provided to ensure research reproducibility. The latest data are updated on an hourly basis, and the vintage data are available since April 14, 2020. All the data are available in CSV files or SQLite format. By unifying the access to the data, this work makes it possible to study the pandemic on a global scale with high resolution, taking into account within-country variations, nonpharmaceutical interventions, and environmental and exogenous variables. Nature Publishing Group UK 2022-03-29 /pmc/articles/PMC8964767/ /pubmed/35351921 http://dx.doi.org/10.1038/s41597-022-01245-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Guidotti, Emanuele A worldwide epidemiological database for COVID-19 at fine-grained spatial resolution |
title | A worldwide epidemiological database for COVID-19 at fine-grained spatial resolution |
title_full | A worldwide epidemiological database for COVID-19 at fine-grained spatial resolution |
title_fullStr | A worldwide epidemiological database for COVID-19 at fine-grained spatial resolution |
title_full_unstemmed | A worldwide epidemiological database for COVID-19 at fine-grained spatial resolution |
title_short | A worldwide epidemiological database for COVID-19 at fine-grained spatial resolution |
title_sort | worldwide epidemiological database for covid-19 at fine-grained spatial resolution |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8964767/ https://www.ncbi.nlm.nih.gov/pubmed/35351921 http://dx.doi.org/10.1038/s41597-022-01245-1 |
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