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HIT-COVID, a global database tracking public health interventions to COVID-19

The COVID-19 pandemic has sparked unprecedented public health and social measures (PHSM) by national and local governments, including border restrictions, school closures, mandatory facemask use and stay at home orders. Quantifying the effectiveness of these interventions in reducing disease transmi...

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Autores principales: Zheng, Qulu, Jones, Forrest K., Leavitt, Sarah V., Ung, Lawson, Labrique, Alain B., Peters, David H., Lee, Elizabeth C., Azman, Andrew S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7453020/
https://www.ncbi.nlm.nih.gov/pubmed/32855428
http://dx.doi.org/10.1038/s41597-020-00610-2
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author Zheng, Qulu
Jones, Forrest K.
Leavitt, Sarah V.
Ung, Lawson
Labrique, Alain B.
Peters, David H.
Lee, Elizabeth C.
Azman, Andrew S.
author_facet Zheng, Qulu
Jones, Forrest K.
Leavitt, Sarah V.
Ung, Lawson
Labrique, Alain B.
Peters, David H.
Lee, Elizabeth C.
Azman, Andrew S.
author_sort Zheng, Qulu
collection PubMed
description The COVID-19 pandemic has sparked unprecedented public health and social measures (PHSM) by national and local governments, including border restrictions, school closures, mandatory facemask use and stay at home orders. Quantifying the effectiveness of these interventions in reducing disease transmission is key to rational policy making in response to the current and future pandemics. In order to estimate the effectiveness of these interventions, detailed descriptions of their timelines, scale and scope are needed. The Health Intervention Tracking for COVID-19 (HIT-COVID) is a curated and standardized global database that catalogues the implementation and relaxation of COVID-19 related PHSM. With a team of over 200 volunteer contributors, we assembled policy timelines for a range of key PHSM aimed at reducing COVID-19 risk for the national and first administrative levels (e.g. provinces and states) globally, including details such as the degree of implementation and targeted populations. We continue to maintain and adapt this database to the changing COVID-19 landscape so it can serve as a resource for researchers and policymakers alike.
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spelling pubmed-74530202020-09-03 HIT-COVID, a global database tracking public health interventions to COVID-19 Zheng, Qulu Jones, Forrest K. Leavitt, Sarah V. Ung, Lawson Labrique, Alain B. Peters, David H. Lee, Elizabeth C. Azman, Andrew S. Sci Data Data Descriptor The COVID-19 pandemic has sparked unprecedented public health and social measures (PHSM) by national and local governments, including border restrictions, school closures, mandatory facemask use and stay at home orders. Quantifying the effectiveness of these interventions in reducing disease transmission is key to rational policy making in response to the current and future pandemics. In order to estimate the effectiveness of these interventions, detailed descriptions of their timelines, scale and scope are needed. The Health Intervention Tracking for COVID-19 (HIT-COVID) is a curated and standardized global database that catalogues the implementation and relaxation of COVID-19 related PHSM. With a team of over 200 volunteer contributors, we assembled policy timelines for a range of key PHSM aimed at reducing COVID-19 risk for the national and first administrative levels (e.g. provinces and states) globally, including details such as the degree of implementation and targeted populations. We continue to maintain and adapt this database to the changing COVID-19 landscape so it can serve as a resource for researchers and policymakers alike. Nature Publishing Group UK 2020-08-27 /pmc/articles/PMC7453020/ /pubmed/32855428 http://dx.doi.org/10.1038/s41597-020-00610-2 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.
spellingShingle Data Descriptor
Zheng, Qulu
Jones, Forrest K.
Leavitt, Sarah V.
Ung, Lawson
Labrique, Alain B.
Peters, David H.
Lee, Elizabeth C.
Azman, Andrew S.
HIT-COVID, a global database tracking public health interventions to COVID-19
title HIT-COVID, a global database tracking public health interventions to COVID-19
title_full HIT-COVID, a global database tracking public health interventions to COVID-19
title_fullStr HIT-COVID, a global database tracking public health interventions to COVID-19
title_full_unstemmed HIT-COVID, a global database tracking public health interventions to COVID-19
title_short HIT-COVID, a global database tracking public health interventions to COVID-19
title_sort hit-covid, a global database tracking public health interventions to covid-19
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7453020/
https://www.ncbi.nlm.nih.gov/pubmed/32855428
http://dx.doi.org/10.1038/s41597-020-00610-2
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