Architectures of analytics intelligent decision technologies systems (IDTS) for the COVID-19 pandemic

Document Type

Article

Department or Administrative Unit

IT and Administrative Management

Publication Date

4-18-2022

Abstract

This article presents a selective literature review of Analytics Intelligent Decision Technologies Systems (Analytics IDTS) developed to support decision-making in business and public organizations, with a particular focus on the global COVID-19 pandemic. We select Analytics IDTS published in 2019–2020 and evaluate them with an Analytics IDTS Design and Evaluation Framework. We include the types of Analytics IDTS, their decisional services, architectural capabilities, and support for phases in the decision-making process. Results are shown for 33 articles in the general Analytics domain and 71 articles in the focused Public Health domain applied to COVID-19, including how these Analytics IDTS were architected and utilized for decision making. Research in descriptive and predictive models is evident in Public Health COVID-19 research reflecting the lack of knowledge about the disease, while predictive and prescriptive models are the primary focus of the general Analytics domain. IDTS in all disciplines rely on Algorithmic decision services and Heuristic Analysis services. Higher-level decisional Synthesis and Hybrid services such as design, explanations, discovery, and learning associated with human decision-making are missing in most types of decision support, indicating that research in Machine Learning and AI has many growth opportunities for future research.

Comments

This article was originally published in Intelligent Decision Technologies. The full-text article from the publisher can be found here.

Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.

Journal

Intelligent Decision Technologies

Rights

© 2022 – IOS Press

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