An extended picture fuzzy multicriteria group decision analysis with different weights: A case study of COVID-19 vaccine allocation

Tarifa Almulhim*, Igor Barahona

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

The high contagion rates of COVID-19 and the limited amounts of vaccines forced public health authorities to develop vaccinations strategies for minimizing mortality, avoiding the collapse of health care infrastructure, and reducing their negative impacts to societies and economies. We propose a Multi Criteria Group Decision Making for prioritizing a set of COVID-19 vaccination alternatives, under a picture fuzzy environment, where the weights for Decisions Experts (DE) and criteria are unknown. A panel of six DEs assess six criteria for prioritizing four groups for vaccination. The weights for DE and criteria are handled in the form of fuzzy sets. Three types of weights are calculated: subjective, objective, and mixture weights. According to our results, three out of the six criteria hold 60% of the strategic importance: 1) allocation and distribution, 2) COVID-19 strains and 3) capabilities and infrastructures. However, persons with comorbidities became the group with the highest priority, followed by essential workers, women, and adults older than 40 years. Governments, decision makers, and policy makers can find rigorous scientific evidence for articulating effective vaccinations campaigns from this work, and contribute to minimize undesired outputs, such as high mortality rates or collapse of hospitals.

Original languageEnglish
Article number101435
JournalSocio-Economic Planning Sciences
Volume85
DOIs
StatePublished - Feb 2023

Bibliographical note

Publisher Copyright:
© 2022 The Authors

Keywords

  • COPRAS
  • COVID-19
  • Multicriteria group decision making
  • Picture fuzzy environment
  • Vaccination

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Economics and Econometrics
  • Strategy and Management
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research

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