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Novel dynamic fuzzy Decision-Making framework for COVID-19 vaccine dose recipients

Albahri OS 1 , Zaidan AA 1 , Albahri AS 2 , Alsattar HA 1 , Mohammed R 1 , Aickelin U 3 Show all authors , Kou G 4 , Jumaah FM 5 , Salih MM 6 , Alamoodi AH 1 , Zaidan BB 1 , Alazab M 7 , Alnoor A 8 , Al-Obaidi JR 9

Affiliations 

  • 1 Faculty of Engineering & IT, British University in Dubai, United Arab Emirates
  • 2 Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
  • 3 School of Computing and Information Systems, University of Melbourne, 700 Swanston Street, Victoria 3010 Australia
  • 4 School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, China
  • 5 Department of Advanced Applications and Embedded Systems, Intel Corporation, Plot 6 Bayan Lepas Technoplex, 11900, Georgetown, Pulau Pinang, Malaysia
  • 6 Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit, Iraq
  • 7 College of Engineering, IT and Environment, Charles Darwin University, NT, Australia
  • 8 School of Management, Universiti Sains Malaysia, Pulau Pinang, Malaysia
  • 9 Department of Biology, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak 35900, Malaysia
J Adv Res, 2022 Mar;37:147-168.
PMID: 35475277 DOI: 10.1016/j.jare.2021.08.009

Abstract

INTRODUCTION: The vaccine distribution for the COVID-19 is a multicriteria decision-making (MCDM) problem based on three issues, namely, identification of different distribution criteria, importance criteria and data variation. Thus, the Pythagorean fuzzy decision by opinion score method (PFDOSM) for prioritising vaccine recipients is the correct approach because it utilises the most powerful MCDM ranking method. However, PFDOSM weighs the criteria values of each alternative implicitly, which is limited to explicitly weighting each criterion. In view of solving this theoretical issue, the fuzzy-weighted zero-inconsistency (FWZIC) can be used as a powerful weighting MCDM method to provide explicit weights for a criteria set with zero inconstancy. However, FWZIC is based on the triangular fuzzy number that is limited in solving the vagueness related to the aforementioned theoretical issues.

OBJECTIVES: This research presents a novel homogeneous Pythagorean fuzzy framework for distributing the COVID-19 vaccine dose by integrating a new formulation of the PFWZIC and PFDOSM methods.

METHODS: The methodology is divided into two phases. Firstly, an augmented dataset was generated that included 300 recipients based on five COVID-19 vaccine distribution criteria (i.e., vaccine recipient memberships, chronic disease conditions, age, geographic location severity and disabilities). Then, a decision matrix was constructed on the basis of an intersection of the 'recipients list' and 'COVID-19 distribution criteria'. Then, the MCDM methods were integrated. An extended PFWZIC was developed, followed by the development of PFDOSM.

RESULTS: (1) PFWZIC effectively weighted the vaccine distribution criteria. (2) The PFDOSM-based group prioritisation was considered in the final distribution result. (3) The prioritisation ranks of the vaccine recipients were subject to a systematic ranking that is supported by high correlation results over nine scenarios of the changing criteria weights values.

CONCLUSION: The findings of this study are expected to ensuring equitable protection against COVID-19 and thus help accelerate vaccine progress worldwide.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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