ArticleInternational journal of intelligent systems2020
Emergency decision support modeling for COVID-19 based on spherical fuzzy information.
Article in International journal of intelligent systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed.
- An intelligent decision model for optimizing industrial power consumption using q-fraction fuzzy information.Scientific reports · 2026Article
- A robust framework for evaluating green mines towards sustainable development.Scientific reports · 2025Article
- Scenario construction and evolutionary analysis of nonconventional public health emergencies based on Bayesian networks.Frontiers in public health · 2025Article
- An efficient spherical fuzzy MEREC-CoCoSo approach based on novel score function and aggregation operators for group decision making.Granular computing · 2023Article
- Evaluation of economic development policies using a spherical fuzzy extended TODIM model with Z̆-numbers.PloS one · 2023Article
- Novel Information Measures for Fermatean Fuzzy Sets and Their Applications to Pattern Recognition and Medical Diagnosis.Computational intelligence and neuroscience · 2023Article
- A fuzzy approach to support decision-making in the triage process for suspected COVID-19 patients in Brazil.Applied soft computing · 2022Article
- Mathematical modeling and AI based decision making for COVID-19 suspects backed by novel distance and similarity measures on plithogenic hypersoft sets.Artificial intelligence in medicine · 2022Article
- A novel spherical fuzzy AHP-VIKOR methodology to determine serving petrol station selection during COVID-19 lockdown: A pilot study for İstanbul.Socio-economic planning sciences · 2022Article
- The applications of MCDM methods in COVID-19 pandemic: A state of the art review.Applied soft computing · 2022Review
- Rise of multiattribute decision-making in combating COVID-19: A systematic review of the state-of-the-art literature.International journal of intelligent systems · 2022Article
- Public health emergency decision-making and management system sound research using rough set attribute reduction and blockchain.Scientific reports · 2022Article
- Power Muirhead mean in spherical normal fuzzy environment and its applications to multi-attribute decision-making: Spherical normal fuzzy power Muirhead mean.Complex & intelligent systems · 2022Article
- A new approach toJournal of ambient intelligence and humanized computing · 2022Article
- Decision support system for ranking relevant indicators for reopening strategies following COVID-19 lockdowns.Quality & quantity · 2022Article
- EDAS method for decision support modeling under the Pythagorean probabilistic hesitant fuzzy aggregation information.Journal of ambient intelligence and humanized computing · 2022Article
- A collaborative emergency decision making approach based on BWM and TODIM under interval 2-tuple linguistic environment.International journal of machine learning and cybernetics · 2022Article
- Emergency decision support modeling under generalized spherical fuzzy Einstein aggregation information.Journal of ambient intelligence and humanized computing · 2022Article
- Multi-criteria decision-making for coronavirus disease 2019 applications: a theoretical analysis review.Artificial intelligence review · 2022Article
- New method for emergency decision making with an integrated regret theory-EDAS method in 2-tuple spherical linguistic environment.Applied intelligence (Dordrecht, Netherlands) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.