ArticleJournal of infection and public health2021
Based on T-spherical fuzzy environment: A combination of FWZIC and FDOSM for prioritising COVID-19 vaccine dose recipients.
Article in Journal of infection and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Fuzzy APPSS: A novel method for quantifying COVID-19 impact in India under triangular spherical fuzzy environment.Scientific reports · 2024Article
- Quantifying holistic capacity response and healthcare resilience in tackling COVID-19: Assessment of country capacity by MCDM.PloS one · 2024Article
- Development of hybrid feature learner model integrating FDOSM for golden subject identification in motor imagery.Physical and engineering sciences in medicine · 2023Article
- Intelligent Emotion and Sensory Remote Prioritisation for Patients with Multiple Chronic Diseases.Sensors (Basel, Switzerland) · 2023Article
- Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions.Complex & intelligent systems · 2023Article
- An extended picture fuzzy multicriteria group decision analysis with different weights: A case study of COVID-19 vaccine allocation.Socio-economic planning sciences · 2023Article
- A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology.International journal of telemedicine and applications · 2023Review
- Hospital selection framework for remote MCD patients based on fuzzy q-rung orthopair environment.Neural computing & applications · 2023Article
- The applications of MCDM methods in COVID-19 pandemic: A state of the art review.Applied soft computing · 2022Review
- Accuracy of Teledentistry for Diagnosis and Treatment Planning of Pediatric Patients during COVID-19 Pandemic.International journal of telemedicine and applications · 2022Article
- Multi-criteria decision-making for coronavirus disease 2019 applications: a theoretical analysis review.Artificial intelligence review · 2022Article
- Hybrid Diagnosis Models for Autism Patients Based on Medical and Sociodemographic Features Using Machine Learning and Multicriteria Decision-Making (MCDM) Techniques: An Evaluation and Benchmarking Framework.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
- Retraction · 2025-11-14Concerns/Issues about Authorship/Affiliation · Concerns/Issues about Referencing/Attributions · Investigation by Journal/Publisher · Investigation by Third Party · Unreliable Results and/or Conclusions · · see also: https://pubpeer.com/publications/0CFA26B0DB84A9A48130E115E816AE
- Retracted
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The problem complexity of multi-criteria decision-making (MCDM) has been raised in the distribution of coronavirus disease 2019 (COVID-19) vaccines, which required solid and robust MCDM methods. Compared with other MCDM methods, the fuzzy-weighted zero-inconsistency (FWZIC) method and fuzzy decision by opinion score method (FDOSM) have demonstrated their solidity in solving different MCDM challenges. However, the fuzzy sets used in these methods have neglected the refusal concept and limited the restrictions on their constants. To end this, considering the advantage of the T-spherical fuzzy sets (T-SFSs) in handling the uncertainty in the data and obtaining information with more degree of freedom, this study has extended FWZIC and FDOSM methods into the T-SFSs environment (called T-SFWZIC and T-SFDOSM) to be used in the distribution of COVID-19 vaccines. The methodology was formulated on the basis of decision matrix adoption and development phases. The first phase described the adopted decision matrix used in the COVID-19 vaccine distribution. The second phase presented the sequential formulation steps of T-SFWZIC used for weighting the distribution criteria followed by T-SFDOSM utilised for prioritising the vaccine recipients. Results revealed the following: (1) T-SFWZIC effectively weighted the vaccine distribution criteria based on several parameters including T = 2, T = 4, T = 6, T = 8, and T = 10. Amongst all parameters, the age criterion received the highest weight, whereas the geographic locations severity criterion has the lowest weight. (2) According to the T parameters, a considerable variance has occurred on the vaccine recipient orders, indicating that the existence of T values affected the vaccine distribution. (3) In the individual context of T-SFDOSM, no unique prioritisation was observed based on the obtained opinions of each expert. (4) The group context of T-SFDOSM used in the prioritisation of vaccine recipients was considered the final distribution result as it unified the differences found in an individual context. The evaluation was performed based on systematic ranking assessment and sensitivity analysis. This evaluation showed that the prioritisation results based on each T parameter were subject to a systematic ranking that is supported by high correlation results over all discussed scenarios of changing criteria weights values.
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