Assessing and Comparing COVID-19 Intervention Strategies Using a Varying Partial Consensus Fuzzy Collaborative Intelligence Approach
Hsin-Chieh Wu,
Yu-Cheng Wang and
Tin-Chih Toly Chen
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Hsin-Chieh Wu: Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, Taiwan
Yu-Cheng Wang: Department of Aeronautical Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan
Tin-Chih Toly Chen: Department of Industrial Engineering and Management National Chiao Tung University 1001, University Road, Hsinchu 30010, Taiwan
Mathematics, 2020, vol. 8, issue 10, 1-23
Abstract:
The COVID-19 pandemic has severely impacted our daily lives. For tackling the COVID-19 pandemic, various intervention strategies have been adopted by country (or city) governments around the world. However, whether an intervention strategy will be successful, acceptable, and cost-effective or not is still questionable. To address this issue, a varying partial consensus fuzzy collaborative intelligence approach is proposed in this study to assess an intervention strategy. In the varying partial consensus fuzzy collaborative intelligence approach, multiple decision makers express their judgments on the relative priorities of factors critical to an intervention strategy. If decision makers lack an overall consensus, the layered partial consensus approach is applied to aggregate their judgments for each critical factor. The number of decision makers that reach a partial consensus varies from a critical factor to another. Subsequently, the generalized fuzzy weighted assessment approach is proposed to evaluate the overall performance of an intervention strategy for tackling the COVID-19 pandemic. The proposed methodology has been applied to compare 15 existing intervention strategies for tackling the COVID-19 pandemic.
Keywords: intervention strategy; COVID-19 pandemic; layered partial consensus; fuzzy analytic hierarchy process (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)
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