Unveiling Health Security Patterns in the European Union through a Hybrid Entropy-CoCoSo and K-Means Clustering Framework
Bibliographic record
Abstract
Objectives: This study aimed to examine health security (HeS) patterns across European Union (EU) member states to address intra-regional disparities in health security, align with EU-wide policy objectives, and propose evidence-based recommendations for harmonizing preparedness measures while respecting national sovereignty. Methods: This research employed a hybrid multi-criteria decision-making framework, combining the Entropy Weight Method and Combined Compromise Solution (CoCoSo), to assess and rank EU countries, drawing on six Global Health Security Index indicators. K-means clustering classified countries into three performance tiers: High, Intermediate, and Dangerous. Data from the GHSI (2019, 2021) and the aggregated 2017-2021 period were analyzed to track temporal trends and cross-regional performance disparities. A comparative analysis of HeS priorities with the African and Eastern Mediterranean (EMR) Regions further contextualized the EU's HeS landscape. Results: Detection and Reporting, and Rapid Response emerged as the most critical dimensions influencing performance, while Risk Environment and Compliance with Norms showed minimal differentiation. High-performing countries, such as Finland and Germany, demonstrated resilience in surveillance and rapid response, while lower-tier nations, Cyprus, Luxembourg, Malta, and Romania, exhibited systemic vulnerabilities in biosecurity and emergency planning. Post-2019, health system resilience gained prominence, while compliance and risk environment remained neglected. The temporal analysis highlighted post-pandemic shifts in health system disparities. Cross-regional comparisons underscoring context-specific challenges. Conclusion: This study highlights the need for targeted investments in surveillance systems, laboratory infrastructure, and crisis preparedness to address specific gaps in different clusters. A metrics-driven framework can reduce regional disparities, promoting equity in preparedness. Policymakers should adopt a collaborative approach to mitigate crises, using high-performing clusters as benchmarks.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".