A Computational Framework for Vaccine Selection: Comparing MEREC and CRITIC Weighting Techniques

The COVID-19 pandemic highlighted the importance of proper vaccine selection in controlling virus transmission and saving lives. Vaccine selection is a complex process that impacts public health, economic recovery, and global equity, requiring equitable decision-making. This study explores the use of multi-criteria decision-making (MCDM) methods—MEREC (Method Based on the Removal Effects of Criteria) and CRITIC (Criteria Importance Through Intercriteria Correlation)—to determine objective weights for evaluating vaccine selection. Computational analyses are conducted to compare the weights derived from both methods, highlighting their strengths and limitations. The WASPAS (Weighted Aggregated Sum Product Assessment) method is also applied to compare vaccine selection scenarios using the criteria weights obtained from MEREC and CRITIC. The study concludes with a practical application of these methods to a vaccine selection problem, demonstrating their effectiveness in supporting informed decision-making. This research contributes to optimizing vaccine selection strategies by integrating theoretical and computational analysis, ensuring preparedness for future pandemics while promoting global health equity.