Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

Khalili, Hamed and Wimmer, Maria A. (2024) Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic. Life, 14 (7). p. 783. ISSN 2075-1729

[thumbnail of life-14-00783.pdf] Text
life-14-00783.pdf - Published Version

Download (1MB)

Abstract

By applying AI techniques to a variety of pandemic-relevant data, artificial intelligence (AI) has substantially supported the control of the spread of the SARS-CoV-2 virus. Along with this, epidemiological machine learning studies of SARS-CoV-2 have been frequently published. While these models can be perceived as precise and policy-relevant to guide governments towards optimal containment policies, their black box nature can hamper building trust and relying confidently on the prescriptions proposed. This paper focuses on interpretable AI-based epidemiological models in the context of the recent SARS-CoV-2 pandemic. We systematically review existing studies, which jointly incorporate AI, SARS-CoV-2 epidemiology, and explainable AI approaches (XAI). First, we propose a conceptual framework by synthesizing the main methodological features of the existing AI pipelines of SARS-CoV-2. Upon the proposed conceptual framework and by analyzing the selected epidemiological studies, we reflect on current research gaps in epidemiological AI toolboxes and how to fill these gaps to generate enhanced policy support in the next potential pandemic.

Item Type: Article
Subjects: Opene Prints > Biological Science
Depositing User: Managing Editor
Date Deposited: 22 Jun 2024 10:53
Last Modified: 22 Jun 2024 10:53
URI: http://geographical.go2journals.com/id/eprint/3655

Actions (login required)

View Item
View Item