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

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.

Analyzing the Impact of Vaccine Availability on Alternative Supplier Selection Amid the COVID-19 Pandemic: A cFGM-FTOPSIS-FWI Approach

“…To tackle this, some fuzzy or probabilistic decision-making methods [ 46 , 47 , 48 ] have been proposed. For example, Wu et al [ 10 ] proposed a fuzzy collaborative intelligence-based fuzzy analytic hierarchy process (FAHP) approach to evaluate and compare fifteen intervention strategies in response to the COVID-19 pandemic. In their methodology, each expert applies the FGM method to evaluate the relative priorities of criteria.…”

“…This task can be regarded as a fuzzy group multi-criteria decision-making problem [ 8 , 9 ] for the following reasons. First, it is clear that there are many uncertainties associated with the COVID-19 pandemic, which are mainly caused by the human intervention [ 10 ]. Fuzzy sets, such as ordinary fuzzy sets [ 11 , 12 ], intuitionistic fuzzy sets [ 13 ], interval type-2 fuzzy sets [ 14 ], and hesitant fuzzy sets [ 15 ], are particularly useful for dealing with this type of uncertainty.…”

An Auto-Weighting Aggregative Fuzzy Collaborative Intelligence Approach for DRAM Yield Forecasting

“…Fuzzy collaborative intelligence has also been applied to fuzzy analytic hierarchy process problems, in which the priorities derived by experts or the overall performances evaluated by them were aggregated using FI [28][29][30][31], PCFI [31][32], evolving (or layered) PCFI (EPCFI or LPCFI) [33], or FWI [34].…”

Assessing the Robustness of a Factory Amid the COVID-19 Pandemic: A Fuzzy Collaborative Intelligence Approach

“…In addition, Singapore’s experience has shown that migrant workers living in dormitories face additional health risks [ 48 ], because they may be from countries with the severe COVID-19 pandemic or be infected while taking airplanes or living in dormitories. Most short-term preventive measures and intervention strategies, such as wearing masks, physical distancing, periodic disinfection, and regular temperature checks, are designed to reduce such risks [ 49 ]. Supply chain breakage: The COVID-19 pandemic has caused the closures of factories around the world, many of which are located in the upstream or midstream segments of supply chains [ 50 ].…”

“…Their combination is not easy to calculate. To tackle such complexity, is approximated with a TFN as [ 49 ]: where , , and denote the minimum, center of gravity (COG) [ 65 ] and maximum of , respectively: as illustrated in Figure 7 . In this way, the defuzzified value of the approximating TFN is equal to .…”