A Dynamic Case-based Reasoning System for Responding to Infectious Disease Outbreaks

Duan, Jinli; Lin, Zhibin; Jiao, Feng; Jiang, Yixian; Chen, Kexing

Abstract

Infectious diseases are a global public health problem, which requires timely and effective responses. This study proposes a novel model that contributes to the development of such responses. First, the problem scenario features of infectious disease emergency scenarios are extracted, and the problem scenario is structurally described. A Markov model is adopted to analyze the scenario evolution of the infectious disease outbreaks. Then, a dynamic case-based reasoning model is built. Different matching algorithms are designed for crisp symbols, crisp numbers, interval numbers, and fuzzy linguistic variables. The similarity between the target scenario and various historical scenarios is calculated. Finally, an optimized dynamic emergency decision guide is provided. An experiment is conducted to test the validity and feasibility of the proposed method. The results suggest that the model can realistically simulate the process of infectious disease outbreaks and quickly match the recorded scenarios to generate effective and real-time responses.

Citation

Duan, J., Lin, Z., Jiao, F., Jiang, Y., & Chen, K. (2022). A Dynamic Case-based Reasoning System for Responding to Infectious Disease Outbreaks. Expert Systems with Applications, 204, Article 117628. https://doi.org/10.1016/j.eswa.2022.117628

• Journal Article Type: Article
• Acceptance Date: May 17, 2022
• Online Publication Date: May 26, 2022
• Publication Date: Oct 15, 2022
• Deposit Date: May 18, 2022
• Publicly Available Date: May 26, 2023
• Journal: Expert Systems With Applications
• Print ISSN: 0957-4174
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 204
• Article Number: 117628
• DOI: https://doi.org/10.1016/j.eswa.2022.117628
• Public URL: https://durham-repository.worktribe.com/output/1207902