Assessing and comparing covid-19 intervention strategies using a varying partial consensus fuzzy collaborative intelligence approach

Hsin Chieh Wu, Yu Cheng Wang, Tin Chih Toly Chen*

*此作品的通信作者

研究成果: Article同行評審

25 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號1725
頁(從 - 到)1-23
頁數23
期刊Mathematics
8
發行號10
DOIs
出版狀態Published - 10月 2020

指紋

深入研究「Assessing and comparing covid-19 intervention strategies using a varying partial consensus fuzzy collaborative intelligence approach」主題。共同形成了獨特的指紋。

引用此