Complex Fermatean Fuzzy Models and Their Algebraic Aggregation Operators in Decision-Making: A Case Study on COVID-19 Vaccine Selection

Abstract

The COVID-19 pandemic has prompted extensive modeling efforts worldwide, aimed at understanding its progression and the myriad factors influencing its spread across diverse communities. The necessity for tailored control measures, varying significantly by region, became apparent early in the pandemic, leading to the implementation of diverse strategies to manage the virus both in the short and long term. The World Health Organization (WHO) has faced considerable challenges in mitigating the impact of COVID-19, necessitating adaptable and localized public health responses. Traditional mathematical models, often employing classical integer-order derivatives with real numbers, have been instrumental in analyzing the virus's spread; however, these models inadequately address the fading memory effects inherent in such complex scenarios. To overcome these limitations, fuzzy sets (FSs) were introduced, offering a robust framework for managing the uncertainty that characterizes the pandemic’s dynamics. This research introduces innovative methods based on complex Fermatean FSs (CFFSs), alongside their corresponding geometric aggregation operators, including the complex Fermatean fuzzy weighted geometric aggregation (CFFWGA) operator, the complex Fermatean fuzzy ordered weighted geometric aggregation (CFFOWGA) operator, and the complex Fermatean fuzzy hybrid geometric aggregation (CFFHGA) operator. These advanced techniques are proposed as effective tools in the strategic decision-making process for reducing the spread of COVID-19. A compelling case study on COVID-19 vaccine selection was presented, demonstrating the practical applicability and superiority of these methods, effectively bridging theoretical models with real-world applications.