A structured open dataset of government interventions in response to COVID-19
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Published:2020-08-27
Issue:1
Volume:7
Page:
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ISSN:2052-4463
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Container-title:Scientific Data
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language:en
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Short-container-title:Sci Data
Author:
Desvars-Larrive AmélieORCID, Dervic ElmaORCID, Haug NilsORCID, Niederkrotenthaler Thomas, Chen JiayingORCID, Di Natale Anna, Lasser Jana, Gliga Diana S., Roux Alexandra, Sorger Johannes, Chakraborty Abhijit, Ten Alexandr, Dervic AlijaORCID, Pacheco Andrea, Jurczak Ania, Cserjan David, Lederhilger Diana, Bulska Dominika, Berishaj Dorontinë, Tames Erwin FloresORCID, Álvarez Francisco S.ORCID, Takriti Huda, Korbel JanORCID, Reddish Jenny, Grzymała-Moszczyńska Joanna, Stangl Johannes, Hadziavdic Lamija, Stoeger Laura, Gooriah LeanaORCID, Geyrhofer LukasORCID, Ferreira Marcia R., Bartoszek Marta, Vierlinger Rainer, Holder Samantha, Haberfellner Simon, Ahne Verena, Reisch Viktoria, Servedio Vito D. P., Chen Xiao, Pocasangre-Orellana Xochilt MaríaORCID, Garncarek Zuzanna, Garcia DavidORCID, Thurner Stefan
Abstract
AbstractIn response to the COVID-19 pandemic, governments have implemented a wide range of non-pharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset until the end of December 2020.
Funder
Vienna Science and Technology Fund
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability
Reference34 articles.
1. World Health Organization. Tracking Public Health and Social Measures A Global Dataset. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/phsm (2020). 2. Anderson, R. M., Heesterbeek, H., Klinkenberg, D. & Hollingsworth, T. D. How will country-based mitigation measures influence the course of the COVID-19 epidemic? Lancet 395, 931–934 (2020). 3. Ugarov, A. Inclusive costs of NPI measures for COVID-19 pandemic: three approaches. Preprint at https://doi.org/10.1101/2020.03.26.20044552 (2020). 4. Studdert, D. M. & Hall, M. A. Disease control, civil liberties, and mass testing — Calibrating restrictions during the Covid-19 pandemic. N. Engl. J. Med. 383, 102–104 (2020). 5. Pan, A. et al. Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China. JAMA 323, 1915–1923 (2020).
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