Department of Software, Sejong University, 209 Neungdong-ro, Seoul, Republic of Korea.
Department of Data Science, Sejong University, Seoul, Republic of Korea.
J Med Internet Res. 2021 Jun 1;23(6):e29730. doi: 10.2196/29730.
Since the declaration of COVID-19 as a global pandemic by the World Health Organization, the disease has gained momentum with every passing day. Various private and government sectors of different countries allocated funding for research in multiple capacities. A significant portion of efforts has been devoted to information technology and service infrastructure development, including research on developing intelligent models and techniques for alerts, monitoring, early diagnosis, prevention, and other relevant services. As a result, many information resources have been created globally and are available for use. However, a defined structure to organize these resources into categories based on the nature and origin of the data is lacking.
This study aims to organize COVID-19 information resources into a well-defined structure to facilitate the easy identification of a resource, tracking information workflows, and to provide a guide for a contextual dashboard design and development.
A sequence of action research was performed that involved a review of COVID-19 efforts and initiatives on a global scale during the year 2020. Data were collected according to the defined structure of primary, secondary, and tertiary categories. Various techniques for descriptive statistical analysis were employed to gain insights into the data to help develop a conceptual framework to organize resources and track interactions between different resources.
Investigating diverse information at the primary, secondary, and tertiary levels enabled us to develop a conceptual framework for COVID-19-related efforts and initiatives. The framework of resource categorization provides a gateway to access global initiatives with enriched metadata, and assists users in tracking the workflow of tertiary, secondary, and primary resources with relationships between various fragments of information. The results demonstrated mapping initiatives at the tertiary level to secondary level and then to the primary level to reach firsthand data, research, and trials.
Adopting the proposed three-level structure allows for a consistent organization and management of existing COVID-19 knowledge resources and provides a roadmap for classifying future resources. This study is one of the earliest studies to introduce an infrastructure for locating and placing the right information at the right place. By implementing the proposed framework according to the stated guidelines, this study allows for the development of applications such as interactive dashboards to facilitate the contextual identification and tracking of interdependent COVID-19 knowledge resources.
自世界卫生组织宣布 COVID-19 为全球大流行以来，该疾病的传播速度日益加快。各国的私营部门和政府部门都为多方面的研究提供了资金。其中相当一部分努力致力于信息技术和服务基础设施的开发，包括开发用于警报、监测、早期诊断、预防和其他相关服务的智能模型和技术的研究。因此，全球范围内产生了许多信息资源，并可供使用。然而，缺乏一种将这些资源根据数据的性质和来源组织成类别定义明确的结构。
本研究旨在将 COVID-19 信息资源组织成一个定义明确的结构，以方便识别资源、跟踪信息工作流程，并为上下文仪表板的设计和开发提供指导。
进行了一系列的行动研究，回顾了 2020 年全球范围内的 COVID-19 工作和举措。根据初级、中级和高级类别定义的结构收集数据。采用各种描述性统计分析技术深入了解数据，以帮助开发一个概念框架来组织资源并跟踪不同资源之间的交互。
调查初级、中级和高级层面的不同信息使我们能够为 COVID-19 相关工作和举措开发一个概念框架。该资源分类框架提供了一个门户，可以访问带有丰富元数据的全球举措，并帮助用户跟踪三级、二级和一级资源的工作流程，以及各种信息片段之间的关系。结果表明，从三级举措映射到二级举措，再映射到一级举措，即可获取一手数据、研究和试验。
采用三级结构可以对现有的 COVID-19 知识资源进行一致的组织和管理，并为未来资源的分类提供路线图。本研究是最早引入定位和放置正确信息的基础设施的研究之一。通过按照所述准则实施所提出的框架，本研究允许开发交互式仪表板等应用程序，以方便上下文识别和跟踪相互依存的 COVID-19 知识资源。
