Evidence map›Paper›PMID 34538731›Full record

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.

M A Alsalem, H A Alsattar, A S Albahri, R T Mohammed, O S Albahri, A A Zaidan, Alhamzah Alnoor, A H Alamoodi, Sarah Qahtan, B B Zaidan and 3 more

RetractedAbstract readRetracted Publication
In one paragraph

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 It has been retracted, and should not be counted.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.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

M A AlsalemDepartment of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia.
H A AlsattarDepartment of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia.
A S AlbahriInformatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq.
R T MohammedDepartment of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia; Faculty of Computing and Innovative Technology, Geomatika University College, Kuala Lumpur, Malaysia.
O S AlbahriDepartment of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia.
A A ZaidanDepartment of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia. Electronic address: aws.alaa@fskik.upsi.edu.my.
Alhamzah AlnoorSchool of Management, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia.
A H AlamoodiDepartment of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia.
Sarah QahtanFaculty of Computer Science and Information Technology, Universiti Putra Malaysia, Seri Kembangan, Malaysia.
B B ZaidanDepartment of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim 35900, Malaysia; Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan.
Uwe AickelinSchool of Computing and Information Systems, University of Melbourne, 700 Swanston Street, Victoria 3010, Australia.
Mamoun AlazabCollege of Engineering, IT and Environment, Charles Darwin University, NT, Australia.
F M JumaahDepartment of Advanced Applications and Embedded Systems, Intel Corporation, Plot 6 Bayan Lepas Technoplex, 11900 Pulau Pinang, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

COVID-19COVID-19 VaccinesDecision MakingFuzzy LogicHumansSARS-CoV-2COVID-19 VaccinesCOVID-19FDOSMFWZICMulti-criteria decision-makingT-spherical fuzzy setsVaccine

Identifiers

PMID34538731
PMCPMC8388152

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.