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- Title
HIT-COVID, a global database tracking public health interventions to COVID-19.
- Authors
Zheng, Qulu; Jones, Forrest K.; Leavitt, Sarah V.; Ung, Lawson; Labrique, Alain B.; Peters, David H.; Lee, Elizabeth C.; Azman, Andrew S.; HIT-COVID Collaboration; Adhikari, Binita; Wahl, Brian; Sarnowski, Chloé; Antiporta, Daniel A.; Erchick, Daniel J.; Perez-Saez, Javier; Ssekasanvu, Joseph; Lee, Kyu Han; White, Laura; Kostandova, Natalya S.; Menezes, Neia Prata
- Abstract
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. Measurement(s) Public Health • Preventive Intervention Technology Type(s) digital curation Factor Type(s) country • date Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12724058
- Subjects
COVID-19 pandemic; PUBLIC health; STAY-at-home orders; PREVENTION of infectious disease transmission; METADATA
- Publication
Scientific Data, 2020, Vol 7, Issue 1, pN.PAG
- ISSN
2052-4463
- Publication type
Article
- DOI
10.1038/s41597-020-00610-2