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Assessing and Comparing COVID-19 Intervention Strategies Using a Varying Partial Consensus Fuzzy Collaborative Intelligence Approach

Author

Listed:
  • 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)

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.

Suggested Citation

  • Hsin-Chieh Wu & Yu-Cheng Wang & Tin-Chih Toly Chen, 2020. "Assessing and Comparing COVID-19 Intervention Strategies Using a Varying Partial Consensus Fuzzy Collaborative Intelligence Approach," Mathematics, MDPI, vol. 8(10), pages 1-23, October.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:10:p:1725-:d:424521
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    References listed on IDEAS

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    Cited by:

    1. Jeon, Jeonghwan & Suvitha, Krishnan & Arshad, Noreen Izza & Kalaiselvan, Samayan & Narayanamoorthy, Samayan & Ferrara, Massimiliano & Ahmadian, Ali, 2023. "A probabilistic hesitant fuzzy MCDM approach to evaluate India’s intervention strategies against the COVID-19 pandemic," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).

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