COVID-19开放数据:一项生态研究和国际合作,研究北部边缘北极国家的大流行趋势。

IF 2.3 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Health Informatics Journal Pub Date : 2025-04-01 Epub Date: 2025-05-09 DOI:10.1177/14604582251315588
Michael E O'Callaghan, Monica Casey, Dana Pearl, Olivia Hickey, Anette Fosse, Sigurður E Sigurðsson, David W Savage, Katri Vehviläinen-Julkunen, Kirsi Bykachev, Anndra Parviainen, Holly Parker, Joan Condell, Gerry Leavey, Nigel Hart, Pál Weihe, Maria S Petersen, Liam Glynn
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引用次数: 0

摘要

在COVID-19大流行的早期阶段,证据的产生落后于公共卫生应对措施。这项研究描述了一线临床医生的国际合作,他们使用描述COVID-19趋势的开放数据来生成“基于实践的证据”。方法:使用开源编程语言“R”利用来自9个北部边缘和北极(NPA)国家的开放数据资源,并将我们的合作分析和见解发布在面向公众的网站上。该网站的可视化指导了2020年9月至2021年3月期间的电话会议讨论,重点关注国家应对措施的背景,特别是在农村地区。结果:该项目促进了对COVID-19趋势的共同学习,并突出了国家应对措施的关键方面。值得注意的是,在大流行的第一年,农村地区的COVID-19病例和死亡率较低。结论:在开放数据分析的推动下,这一国际合作努力提供了一个分享现实世界见解的平台。该研究为未来的流行病提供了一个潜在的模板,并强调了保持开放数据资源的重要性,包括超额死亡率等细粒度数据,以有效地了解流行病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
COVID-19 open data: An ecological study and international collaboration examining pandemic trends in Northern Periphery arctic countries.

Objectives: In the early stages of the COVID-19 pandemic, evidence generation lagged behind public health responses. This study describes an international collaboration of frontline clinicians who used open data describing COVID-19 trends to generate "practice-based evidence". Methods: Open data resources from nine Northern Periphery and Arctic (NPA) countries were harnessed using the open-source programming language 'R' and our collaborations analyses and insights were published on a public-facing website. The website's visualisations guided teleconference discussions from September 2020 to March 2021, focusing on contextualizing national responses, especially in rural regions. Results: This project facilitated shared learning from COVID-19 trends and highlighted key aspects of national responses. Notably, rural NPA regions experienced less COVID-19 cases and mortality in the first year of the pandemic. Conclusion: This international collaborative effort, driven by open data analysis, provided a platform to share real-world insights. The study offers a potential template for future pandemics and emphasises the importance of sustaining open data resources, including granular data like excess mortality, for effective pandemic learning.

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来源期刊
Health Informatics Journal
Health Informatics Journal HEALTH CARE SCIENCES & SERVICES-MEDICAL INFORMATICS
CiteScore
7.80
自引率
6.70%
发文量
80
审稿时长
6 months
期刊介绍: Health Informatics Journal is an international peer-reviewed journal. All papers submitted to Health Informatics Journal are subject to peer review by members of a carefully appointed editorial board. The journal operates a conventional single-blind reviewing policy in which the reviewer’s name is always concealed from the submitting author.
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