Authors:

• Poonam Mittal, J C Bose University of Science and Technology
• S P Abirami, Vellore Institute of Technology University 
• Puppala Ramya, Koneru Lakshmaiah Education Foundation 
• Balajee J, Mother Theresa Institute of Engineering and Technology
• Elangovan Muniyandy, Saveetha School of Engineering

Introduction

The rapid development and deployment of COVID-19 vaccines have been pivotal in combating the global pandemic. However, assessing the efficacy of these vaccines across different populations and under varying conditions remains a complex challenge. This novel approach aims to provide a more nuanced analysis of vaccine performance, thereby aiding pharmaceutical manufacturers in enhancing vaccine efficacy and assisting healthcare decision-makers in selecting the most appropriate vaccines.

Research Context and Objective

The primary objective of this research is to develop a fuzzy inference system that can process and analyze diverse data sources, including research papers, news articles, and scientific journals, to evaluate eight key aspects of COVID-19 vaccine efficacy. By leveraging the Mamdani fuzzy logic model, the study seeks to offer a practical tool for improving vaccine effectiveness in real-world scenarios and contribute to the ongoing efforts to manage the COVID-19 pandemic more effectively.

Methodology

The Rule-Based Mamdani Fuzzy Inference System employed in this study uses a set of linguistic rules to model the complex relationships between various factors affecting vaccine efficacy. The system considers inputs such as the rate of breakthrough infections, severity of symptoms post-vaccination, duration of immunity, adaptability to virus variants, side effects, age group effectiveness, underlying health conditions, and geographical distribution of vaccine performance.

Each of these factors is fuzzified, meaning they are expressed in linguistic terms (e.g., low, medium, high) rather than precise numerical values. The Mamdani model then applies a set of predefined rules to these fuzzy inputs to generate an inference, which is subsequently defuzzified to produce a quantitative measure of vaccine efficacy.

The model was calibrated using data extracted from a wide array of sources, ensuring a comprehensive analysis that reflects the multifaceted nature of vaccine performance. The efficacy outcomes were validated against real-world vaccination data to assess the accuracy and reliability of the system.

Results

The Rule-Based Mamdani Fuzzy Inference System demonstrated robust performance in evaluating the efficacy of COVID-19 vaccines. The model's ability to synthesize and analyze a diverse range of inputs provided valuable insights that aligned closely with observed real-world outcomes. Specifically, the system was effective in identifying potential factors that could impact vaccine performance, such as the emergence of new virus variants or variations in immunity duration across different demographics.

By offering a more granular understanding of vaccine efficacy, the system enables pharmaceutical manufacturers to pinpoint areas for improvement in vaccine design and formulation. Additionally, the model's outputs serve as a critical decision-making tool for healthcare providers, allowing them to make informed choices about vaccine selection and distribution strategies.

Conclusion

This study highlights the potential of fuzzy inference systems, particularly the Mamdani model, in addressing the complexities associated with evaluating COVID-19 vaccine efficacy. The Rule-Based Mamdani Fuzzy Inference System provides a versatile and practical approach to analyzing vaccine performance, incorporating a wide array of epidemiological and clinical factors to deliver actionable insights.

As the fight against COVID-19 continues, the application of AI-driven models like the Mamdani Fuzzy Inference System offers a promising avenue for optimizing vaccine efficacy and improving public health outcomes. This research not only contributes to the current pandemic response but also sets the stage for future applications of fuzzy logic in the evaluation of vaccines and other medical interventions.