A Computational Framework for Vaccine Selection: Comparing MEREC and CRITIC Weighting Techniques
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".