Significant emergency measures should be taken until an emergency event occurs. It is understood that the emergency is characterized by limited time and information, harmfulness and uncertainty, and decision‐makers are always critically bound by uncertainty and risk. This paper introduces many novel approaches to addressing the emergency situation of COVID‐19 under spherical fuzzy environment. Fundamentally, the paper includes six main sections to achieve appropriate and accurate measures to address the situation of emergency decision‐making. As the spherical fuzzy set (FS) is a generalized framework of fuzzy structure to handle more uncertainty and ambiguity in decision‐making problems (DMPs). First, we discuss basic algebraic operational laws (AOLs) under spherical FS. In addition, elaborate on the deficiency of existing AOLs and present three cases to address the validity of the proposed novel AOLs under spherical fuzzy settings. Second, we present a list of Einstein aggregation operators (AgOp) based on the Einstein norm to aggregate uncertain information in DMPs. Thirdly, we are introducing two techniques to demonstrate the unknown weight of the criteria. Fourthly, we develop extended TOPSIS and Gray relational analysis approaches based on AgOp with unknown weight information of the criteria. In fifth, we design three algorithms to address the uncertainty and ambiguity information in emergency DMPs. Finally, the numerical case study of the novel carnivorous (COVID‐19) situation is provided as an application for emergency decision‐making based on the proposed three algorithms. Results explore the effectiveness of our proposed methodologies and provide accurate emergency measures to address the global uncertainty of COVID‐19.

中文翻译：

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基于球形模糊信息的COVID-19应急决策支持建模


在紧急事件发生之前，应采取重大应急措施。据了解，突发事件具有时间和信息有限、危害性和不确定性等特点，决策者始终受到不确定性和风险的严格约束。本文介绍了在球形模糊环境下解决 COVID-19 紧急情况的许多新颖方法。基本上，本文包括六个主要部分，以实现针对紧急决策情况的适当和准确的措施。由于球形模糊集（FS）是模糊结构的广义框架，可以处理决策问题（DMP）中更多的不确定性和模糊性。首先，我们讨论球形 FS 下的基本代数运算定律 (AOL)。此外，详细阐述了现有 AOL 的不足，并提出了三个案例来解决所提出的新型 AOL 在球形模糊设置下的有效性。其次，我们提出了基于爱因斯坦范数的爱因斯坦聚合算子（AgOp）列表，用于聚合 DMP 中的不确定信息。第三，我们引入两种技术来证明标准的未知权重。第四，我们开发了基于 AgOp 的扩展 TOPSIS 和灰色关系分析方法，具有未知的标准权重信息。第五，我们设计了三种算法来解决紧急 DMP 中的不确定性和模糊性信息。最后，提供了新型肉食性（COVID-19）情况的数值案例研究，作为基于所提出的三种算法的紧急决策的应用。结果探讨了我们提出的方法的有效性，并提供准确的紧急措施来解决 COVID-19 的全球不确定性。