Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Apr 18, 2021
Date Accepted: May 12, 2021
Date Submitted to PubMed: May 17, 2021

The final, peer-reviewed published version of this preprint can be found here:

COVID-19 Knowledge Resource Categorization and Tracking: Conceptual Framework Study

Afzal M, Hussain M, Hussain J, Lee S

COVID-19 Knowledge Resource Categorization and Tracking: Conceptual Framework Study

J Med Internet Res 2021;23(6):e29730

DOI: 10.2196/29730

PMID: 33999833

PMCID: 8171286

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

COVID-19 Knowledge Resource Categorization and Tracking: A Conceptual Framework

  • Muhammad Afzal; 
  • Maqbool Hussain; 
  • Jamil Hussain; 
  • Sungyoung Lee

ABSTRACT

Background:

Declaring the COVID-19 disease a global pandemic by the World Health Organization (WHO), it gained momentum as every day passed, and private and government sectors of different countries pushed funding towards research in various capacities. A great portion of efforts is devoted to information technology and service infrastructure development, including research to develop intelligent models and techniques for alerts, monitoring, early diagnosis, prevention, and other relevant services. As a result, tons of information resource have been created in the global space and are available for use. However, there is lack of a defined structure to organize these resources into categories or classes based on the nature as well the origin of data.

Objective:

This study aims to organize COVID-19 information resources into a well-defined structure to facilitate easy identification of a resource, tracing information workflows, and a guide for contextual dashboards design and development.

Methods:

A sequence of action research was performed that involve a review of COVID-19 efforts and initiatives on a global scale during the year 2020. Data is collected according to a defined structure of primary, secondary, and tertiary categories. Various descriptive statistical analysis techniques were employed to get insights of the data to help in developing a conceptual framework underlining the organization of resources and interactions among different resources.

Results:

In this paper, we present a three-level structure of resource categorization that provides a gateway to access the global initiatives with enriched metadata, assists users in tracing the workflow of tertiary, secondary, and primary resources with relationships among various fragments of information. The results comprise mapping initiatives at the tertiary level to secondary and then to the primary level to reach the firsthand resource of data, research, and trials.

Conclusions:

Adopting the proposed three-level structure enables a consistent organization and management of existing COVID-19 knowledge resources and provides a roadmap for classifying the futuristic resources. This study is one of the earliest studies to introduce an organized structure and demonstrate the placement of COVID-19 resources at the right place. By implementing the proposed framework according to the stated guidelines, this study facilitates the development of applications such as interactive dashboards to facilitate the contextual identification and tracking of interdependent COVID-19 information resources.


 Citation

Please cite as:

Afzal M, Hussain M, Hussain J, Lee S

COVID-19 Knowledge Resource Categorization and Tracking: Conceptual Framework Study

J Med Internet Res 2021;23(6):e29730

DOI: 10.2196/29730

PMID: 33999833

PMCID: 8171286

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.