Unveiling Health Security Patterns in the European Union through a Hybrid Entropy-CoCoSo and K-Means Clustering Framework

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Nasser" }, { "@type": "Person", "name": "Yahya Ali Al-Samawi" }, { "@type": "Person", "name": "Abed Saif Ahmed Alghawli" }, { "@type": "Person", "name": "Amani A. K. Essayed" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " 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. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-600", "name": "Unveiling Health Security Patterns in the European Union through a..." } } ] } Home Browse Unveiling Health Security Patterns in the European Union through a... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Nasser AA, Al-Samawi YA, Alghawli ASA and Essayed AAK. Unveiling Health Security Patterns in the European Union through a Hybrid Entropy-CoCoSo and K-Means Clustering Framework [version 1; peer review: 2 approved] . F1000Research 2025, 14 :600 ( https://doi.org/10.12688/f1000research.166187.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Unveiling Health Security Patterns in the European Union through a Hybrid Entropy-CoCoSo and K-Means Clustering Framework [version 1; peer review: 2 approved] Adel A. Nasser https://orcid.org/0000-0001-5456-1303 1,2 , Yahya Ali Al-Samawi 3 , Abed Saif Ahmed Alghawli https://orcid.org/0000-0002-6586-2141 4 , Amani A. K. Essayed 4 Adel A. Nasser https://orcid.org/0000-0001-5456-1303 1,2 , Yahya Ali Al-Samawi 3 , Abed Saif Ahmed Alghawli https://orcid.org/0000-0002-6586-2141 4 , Amani A. K. Essayed 4 PUBLISHED 19 Jun 2025 Author details Author details 1 Department of Information Systems and Computer Science, Sa’adah University, Sa’adah University, Sa’adah, Yemen 2 Department of Artificial Intelligence, Modern Specialized University, Sana'a, Yemen 3 Information Technology, Modern Specialized University, Sana'a, Yemen 4 Department of Computer Science, College of Sciences and Humanities, Prince Sattam bin Abdulaziz University, Al Kharj, Riyadh Province, 16700, Saudi Arabia Adel A. Nasser Roles: Conceptualization, Formal Analysis, Methodology, Software, Supervision, Writing – Original Draft Preparation Yahya Ali Al-Samawi Roles: Conceptualization, Investigation, Resources, Validation, Writing – Review & Editing Abed Saif Ahmed Alghawli Roles: Data Curation, Formal Analysis, Funding Acquisition, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Amani A. K. Essayed Roles: Software, Visualization, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Health Services gateway. 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. READ ALL READ LESS Keywords health security, European Union, multi-criteria decision-making, entropy, combined compromise solution, clustering, ranking Corresponding Author(s) Adel A. Nasser ( [email protected] ) Close Corresponding author: Adel A. Nasser Competing interests: No competing interests were disclosed. Grant information: This study was supported via funding from Deanship of Scientific Research, Prince Sattam bin Abdulaziz University [project number: PSAU/2025/R/1446]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Nasser AA et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Nasser AA, Al-Samawi YA, Alghawli ASA and Essayed AAK. Unveiling Health Security Patterns in the European Union through a Hybrid Entropy-CoCoSo and K-Means Clustering Framework [version 1; peer review: 2 approved] . F1000Research 2025, 14 :600 ( https://doi.org/10.12688/f1000research.166187.1 ) First published: 19 Jun 2025, 14 :600 ( https://doi.org/10.12688/f1000research.166187.1 ) Latest published: 19 Jun 2025, 14 :600 ( https://doi.org/10.12688/f1000research.166187.1 ) 1. Introduction Health security (HeS) is a dynamic framework designed to protect populations from both known and emerging health threats. Expanding beyond traditional public health models, HeS integrates proactive disease surveillance, emergency preparedness, and responsive outbreak management while fostering international collaboration and technological innovation for early risk detection and mitigation. 1 Its multi-faceted strategy addresses current challenges while anticipating evolving global health risks. Significant shortcomings in global preparedness for health emergencies were exposed during the COVID-19 crisis, underscoring the urgent need for resilient frameworks that harmonize surveillance, rapid response mechanisms, and equitable resource allocation. 2 – 4 Health security demands continuous adaptation to shifting threats, 5 sustained by iterative monitoring, flexible governance, and prioritized benchmarks to evaluate systems, guide investments, and address vulnerabilities. 5 – 7 However, resource constraints and conflicting national priorities complicate alignment, necessitating multilateral cooperation to optimize regional collaboration and resource allocation. 6 , 8 The Global Health Security Index (GHSI), a joint initiative of the Nuclear Threat Initiative and Johns Hopkins Center for Health Security, measures pandemic preparedness in 195 countries by evaluating performance in six core categories: disease prevention, outbreak detection, crisis response, healthcare system robustness, compliance with global standards, and environmental and societal risk conditions. 9 Despite its global adoption, critics highlight critical flaws in its methodology 5 , 6 , 10 : (1) A rigid scoring system that uniformly weights criteria, ignoring regional disparities; (2) Insufficient granularity for regional and sub-regional ranking, clustering, or dynamic trend analysis; and (3) Unable to capture evolving performance, particularly pre- and post-COVID-19 weightings, rankings, and clustering shifts. These shortcomings significantly constrain the GHSI’s effectiveness as a tool for context-aware, policy-relevant analysis. Employing hybrid multi-method models—such as entropy-TOPSIS-K-means in Africa, 5 entropy-VIKOR-K-means in the Eastern Mediterranean Region (EMR), 6 and D-CRITIC-CoCoSo-K-means in Western Asia 10 —has demonstrated significant value in health security evaluation. These approaches combine objective weighting, robust ranking, and clustering techniques to enable nuanced performance analysis, priority identification, and pattern exploration across diverse national contexts. By sequentially applying these methods, hybrid frameworks offering a more rigorous, interpretable, and context-sensitive foundation for decision-making. 5 , 6 , 10 However, despite their proven effectiveness in other global regions, health security analyses within the EU still lack hybrid frameworks—particularly those tailored to the region’s distinct health dynamics and policy context. Global indices like the GHSI homogenize EU member states within international rankings, overlooking the bloc’s distinct supranational governance structures, such as the European Health Union (EHU). While the EHU mandates cross-border collaboration, its implementation is often fragmented. Unlike regional studies in Africa, 5 the EMR, 6 or Western Asia 10 —which prioritize context-specific challenges like zoonotic spillovers or conflict-driven infrastructure deficits—EU disparities largely stem from uneven policy adoption and variable healthcare resilience. For instance, Forman and Mossialos critique the EU’s lack of harmonized surveillance, 11 and Rees et al. (2024) 12 highlight operational inefficiencies in pandemic response. Parallel research stresses the centrality of resilient health systems and supply chain redundancies in crisis resilience, particularly in mitigating disruptions during acute shocks. 13 , 14 Expanding the discourse, study 15 highlight persistent zoonotic spillover risks, urging preemptive One Health strategies to address interspecies disease transmission. Complementary analyses reinforce calls for integrated, data-centric approaches to health security (HeS). Brown et al. (2022) conducted a systematic review of health system-security linkages, revealing a persistent disconnect between emergency preparedness frameworks and routine healthcare operations. 16 Their findings support the integration of HeS objectives—such as outbreak response protocols—into core health system functions, rather than maintaining them as discrete initiatives. Furthermore, their analysis highlights the need for precise policies that tackle both overarching healthcare deficiencies and the specific contextual factors within EU member states. Region-specific challenges are further elucidated by El Samad et al. (2022), who document resource constraints, demographic shifts, and chronic disease burdens in Mediterranean EU states, proposing data-driven optimization of healthcare delivery to address these pressures. 17 Similarly, Rees et al. (2024) critique heterogeneous pandemic responses across Europe, arguing that operational inefficiencies stem not from a lack of technical systems but from underdeveloped coordination and adaptability mechanisms. 12 These findings collectively underscore the critical need for health security investigations that are specifically tailored to the EU context, accounting for its unique policy landscape, and regional disparities. A failure to do so risks misinterpreting the underlying dynamics and hindering the development of effective, context-appropriate solutions. 6 , 10 Research problem and questions: Existing EU-focused studies lack longitudinal, hybrid multi-method analyses that effectively capture the region’s unique supranational governance structure and address intra-regional disparities in health security policy implementation and resilience. To bridge this gap, this study explores: - How do health security priorities in the EU—such as detection and rapid response—differ from those in non-EU regions (e.g., Africa’s prevention focus, EMR’s health system emphasis)? - What clusters of performance exist among EU member states (2017–2021), and how do these reflect evolving post-pandemic challenges like infrastructure gaps or surveillance fragmentation? - How can supranational bodies leverage ranking and cluster-tiered findings to harmonize preparedness while respecting national autonomy? Research objectives: This research seeks to assess and categorize health security preparedness among European Union (EU) member states by using a hybrid Entropy-CoCoSo and K-Means clustering approach. By identifying key performance indicators and clustering patterns, the research seeks to address intra-regional disparities in health security, align with EU-wide policy objectives, and offer actionable recommendations for harmonizing preparedness measures while respecting national autonomy. Aligned with the EU’s 2017–2021 health security trajectory—spanning pre-pandemic benchmarks under the Joint Action on Health Security to post-COVID-19 reforms like the Health Emergency Preparedness and Response Authority (HERA)—this analysis informs actionable strategies. By identifying underperforming clusters (e.g., Cyprus, Malta), the findings support HERA’s mandate to allocate infrastructure funding and standardize cross-border crisis protocols. Concurrently, the prioritization of detection and rapid response aligns with the EHU’s 2023–2027 strategy to unify surveillance systems, offering a metrics-driven blueprint for reconciling implementation gaps. Scientific contribution and implications: To overcome the limitations of global indices and provide a more granular assessment of EU health security, this study makes several key contributions to the field of health security. It improves research methods by using a combination of Entropy-CoCoSo and K-means clustering to evaluate health security in European Union (EU) member states—something that hasn't been done in this area before. This method expands the body of scientific modeling methodologies applicable to real-world multi-criteria problems, showcasing how hybrid decision-making and clustering techniques can generate actionable insights for evaluating and strengthening health system resilience. By integrating concepts from multi-criteria decision-making (MCDM) and machine learning, the study fosters interdisciplinary innovation, enabling the fusion of theoretical and computational approaches to tackle complex public health challenges. Furthermore, this research promotes goal-oriented science by enabling precise monitoring, evaluation, and acceleration of progress on global health security (GHS) goals. Unlike static global indices like the Global Health Security Index (GHSI), which often overlook regional nuances and temporal dynamics, the used model offers a granular, dynamic, and context-sensitive assessment. It identifies intra-regional disparities and temporal shifts in health security priorities, providing a replicable, scalable framework that supports tailored policy guidance and resource allocation at both national and supranational levels. The study also contributes to a more equitable and effective global health dialogue by highlighting divergent regional priorities between the EU, Africa, and the Eastern Mediterranean Region (EMR). These findings emphasize the need for context-specific policies and the rejection of one-size-fits-all strategies. By recognizing regional heterogeneity in health security drivers—such as detection in the EU, prevention in Africa, and health system robustness in the EMR—the study encourages localized policy frameworks and fosters more equitable partnerships. It also enables donor agencies and multilateral institutions to realign programs with the unique health security realities and development priorities of each region. Additionally, the study maps EU health security priorities and classifies member states into performance-based clusters over the 2017–2021 period, revealing systemic disparities and resilience gaps exposed by the COVID-19 pandemic. This cluster-based assessment facilitates differentiated interventions that respect national autonomy while promoting strategic harmonization through bodies like the European Health Union (EHU) and the Health Emergency Preparedness and Response Authority (HERA). By offering empirical insights and data-driven recommendations, the study equips policymakers with the tools to design forward-looking, region-specific responses to evolving health threats. Finally, the analytical framework established here serves as a foundation for future research, including cross-regional comparisons and subnational analyses, supporting a globally adaptable model for health security evaluation. The remainder of this paper is structured as follows: Section 2 details the hybrid Entropy-CoCoSo-K-means methodology, explaining entropy-based criterion weighting, CoCoSo’s ranking, and K-means clustering for dynamic tier classification. Section 3 evaluates EU health security performance through cross-regional benchmarks (Africa, EMR) and temporal analysis of intra-EU clusters (2019–2021). Section 4 analyzes trends, policy gaps, and actionable recommendations for stakeholders. Section 5 concludes with strategic interventions and future research directions. 2. Methods Multi-Criteria Decision-Making (MCDM) methods have become essential tools across disciplines such as health security, sustainability, tourism, healthcare, engineering, and financial risk management. MCDM has guided strategic planning in various fields—such as prioritizing eco-hotel performance, 18 , 19 comparing hospital health literacy levels, 20 and managing information security in banks. 21 It has also supported healthcare resource allocation and optimization. 22 Globally, MCDM has played a vital role in addressing post-COVID-19 challenges, with applications in vaccine selection, biomedical decision-making, health security evaluation, and sustainability benchmarking. In sustainability, entropy-VIKOR and related models have been used to compare national strategies. 23 , 24 Hybrid MCDM models—such as Entropy-CoCoSo and EDAS—have ranked biomedical materials, 25 evaluated COVID-19 vaccine options, 26 and assessed urban stress levels. 27 In regional health security studies, integrated MCDM and clustering approaches have been employed to evaluate disparities. 5 , 6 , 10 Furthermore, clustering tools such as K-means enhance the scalability of MCDM by enabling the analysis of complex, multidimensional datasets. 5 , 6 , 10 , 28 , 29 Health security assessment can be formulated as a complex Multi-Criteria Decision-Making (MCDM) problem, characterized by dynamic, interconnected, and evolving dimensions that demand robust evaluation frameworks. The need to assess health security improvements over multiple years adds temporal complexity, requiring the consideration of shifting regional trajectories and the comparative prioritization of diverse global settings. 10 Health security itself encompasses a multidimensional network of interdependent targets—such as prevention, detection, response, governance, and international collaboration—each varying in criticality depending on regional vulnerabilities and temporal factors. 9 Prioritizing these targets across regions involves navigating competing criteria, dynamic weightings, and complex trade-offs. Moreover, geographic priority analysis, as another MCDM layer, is critical for systematically identifying systemic weaknesses within regional health security infrastructures and informing the strategic direction of future interventions and research. 6 Thus, solving health security assessment as an MCDM problem requires hybrid, adaptive methodologies capable of integrating heterogeneous, time-sensitive, and interdependent criteria for dynamic and actionable policy development. 5 , 6 , 10 Moreover, objective weighting methods, such as Multi-Criteria Decision-Making (MCDM) techniques, offer robust solutions by assigning adaptable, data-informed weights to health security indicators, reflecting their changing significance over time. 10 In MCDM, objective weighting methods facilitate the nuanced ranking and categorization of indicators based on their importance, 21 , 30 – 32 allowing comparisons across different regions and periods and highlighting those areas most in need of improvement. 6 , 10 By determining indicator importance for specific regions, these techniques enable tailored strategies and interventions that address each region's unique needs and challenges more effectively, allow comparisons between regions, and help identify trends and changes over time. This facilitates more accurate forecasting and long-term planning and promotes a better understanding of regional disparities and potential areas for improvement. 10 Through indicator categorization, resources can be allocated more efficiently based on the relative importance of different factors. 5 , 22 Moreover, the ability to reveal hidden patterns in indicator importance across regions and time can lead to new insights and research directions that might otherwise be overlooked. 6 Furthermore, combining hybrid weighting with ranking MCDM methods and clustering techniques facilitates the detailed categorization of countries based on comprehensive performance profiles, spotlighting those most urgently needing enhancement. 5 , 23 , 24 , 33 , 34 Integrating dynamic analyses further enables continuous tracking of progress and emerging trends, allowing policymakers to make evidence-based decisions that respond flexibly to the evolving landscape of HeS development. 5 , 6 , 10 These methods enable a more holistic evaluation of health security by considering multiple criteria simultaneously. 5 Comparing performance across different time frames allows for the tracking of progress or regression in health security measures over time. 6 Grouping countries by region facilitates the identification of regional trends, strengths, and weaknesses, informing policymakers about areas requiring improvement and helping prioritize resource allocation. 10 Countries can also use these rankings to benchmark their performance against peers and identify best practices. Furthermore, highlighting disparities in health security can encourage global collaboration and support for underperforming countries, increase public awareness of health security issues, and potentially drive citizen engagement. 5 Additionally, the results can guide further research into factors influencing health security performance. In regional MCDM applications across various domains, hybrid models have demonstrated high effectiveness. For example, an Entropy-VIKOR hybrid was successfully applied to select dental restorative materials, demonstrating the method’s capability to rank alternatives effectively in biomedical contexts. 25 Similarly, an Entropy-EDAS integration was employed for diesel engine parameter optimization, validated by other methods like WASPAS and TOPSIS. 35 In the field of health security, MCDM methods have also shown strong applicability. TOPSIS and VIKOR were used to evaluate COVID-19 vaccine selection strategies, considering factors such as safety, cost, and implementation challenges. 26 A notable application during the COVID-19 era is seen in a study on Western Asia, where a D-CRITIC-based MCDM model used the CoCoSo method for ranking countries, followed by K-means clustering to effectively identify varying pandemic readiness levels across nations. 10 Similarly, in Africa and the Eastern Mediterranean Region (EMR), studies combined entropy weighting with TOPSIS and VIKOR models, respectively, alongside K-means clustering to categorize countries by their health security performance. 5 , 6 Additionally, a CRITIC-TOPSIS model was used to assess stress levels in major Indian cities during the COVID-19 outbreak, showing that urban overload and vulnerability could be systematically measured with MCDM techniques. 27 The Entropy-CoCoSo approach has been widely applied in diverse fields, including engineering sustainability selection 36 and spatiotemporal analysis of business environments. 33 These studies collectively demonstrate that MCDM approaches—especially hybrid models integrating entropy, CRITIC, D-CRITIC, TOPSIS, VIKOR, CoCoSo, EDAS, WASPAS, and K-means clustering—have become essential for accurately assessing health security status, identifying risk priorities, and guiding targeted policy interventions across different geographic and socio-economic contexts. Each MCDM method, however, has its own advantages and disadvantages depending on the problem at hand, the nature of available data, and decision-maker preferences. Different MCDA methods may produce conflicting recommendations, leading to potential uncertainty and confusion. Furthermore, they often require distinct types of input data, weighting schemes, and assumptions. In this study, we used the entropy method to address the first research question: determining health security priorities in the EU and comparing them with those derived for Africa. 5 , 6 This decision is based on several key considerations. First, the entropy method is one of the most popular objective weighting techniques for determining criteria weights, widely recognized for its ability to measure variability within data, reduce subjective bias, ensure impartiality, and offer adaptability across diverse fields, including healthcare and health security decision-making. Second, the choice aligns with consistency across comparative studies, as both the African and Eastern Mediterranean Region (EMR) analyses used the entropy method, facilitating a more coherent and valid cross-regional comparison. For the ranking process, we selected the CoCoSo method due to its robustness. CoCoSo ranks alternatives by integrating weighted sum, weighted product, and exponential compromise models, thus minimizing the bias that can result from relying on a single aggregation technique. However, it is important to acknowledge a limitation: the Entropy-CoCoSo model has not yet been widely applied to Global Health Security Index (GHSI)-related frameworks, particularly in combination with entropy weighting. This lack of prior application introduces some uncertainty regarding its comparative performance and interpretability relative to more established methods such as TOPSIS, VIKOR, EDAS, and WASPAS. Finally, for clustering, we selected K-means due to its proven effectiveness in previous health security MCDM studies for grouping countries by performance profiles, enabling clearer identification of regional trends and risk priorities. Thus, the combined Entropy-CoCoSo-K-means approach offers a comprehensive, objective, and innovative methodology for our multi-year evaluation of health security across the EU. Our analysis adhered to a structured workflow comprising five sequential stages. Below, we outline the rationale and execution of each phase. 2.1 Health security indicators and data source integration This study utilizes the Global Health Security Index (GHSI) datasets from 2019 and 2021, 37 developed by the Johns Hopkins Center for Health Security, to evaluate health security performance across EU member states based on six core criteria: Prevention (PRE), Detection and Reporting (D&R), Rapid Response (RRe), Health System (HSy), Adherence to International Norms (AIN), and Contextual Risk Environment Factors (REF). The GHSI is a robust, internationally recognized tool designed to assess countries’ capacities to prevent, detect, and respond to public health threats. Its methodological rigor and comprehensive framework make it uniquely suited for evaluating pandemic preparedness. 5 , 6 , 9 , 10 One key advantage of the GHSI lies in its global standardization, enabling consistent cross-national comparisons using validated, peer-reviewed indicators. 9 This ensures that health security performance is measured through universally accepted criteria grounded in international public health frameworks rather than arbitrary or country-specific metrics. Moreover, the GHSI’s scientific foundation and widespread international adoption as a benchmark for health security have been demonstrated through numerous prior studies conducted across various geographic contexts, further supporting its credibility and relevance. In recognition of this, researchers have conducted statistical analyses of GHSI metrics alongside in-depth evaluations of related policy measures and organizational frameworks. 38 , 39 Multi-criteria decision-making (MCDM) and machine learning-based investigations leveraging the GHSI have been applied effectively in Africa, 5 the Eastern Mediterranean Region (EMR), 6 and Western Asia. 10 These applications highlight the GHSI’s adaptability and reliability as a benchmarking tool across diverse health and governance environments, reinforcing its suitability for the objectives of this study. Taking into account the significance of the GHSI as a tool for health security, and recognizing that one of the primary objectives of this study is to compare weighting across regions, the performance metrics for the main six GHSI indicators were selected as the primary means to assess the weights of the indicators, as well as to rank and cluster EU countries. This study utilized three performance metrics for the years 2019 and 2021, along with the average scores from 2017 to 2021, to analyze the weights, ranking, and clustering of EU countries. As stated previously, this study aims not only to analyze health security priorities within the EU but also to compare the entropy weighting results with prior regional research from the African region 5 and the Eastern Mediterranean Region (EMR). 6 Previous studies calculated weights based solely on the data from 2019 and 2021, without considering the entire time period from 2017 to 2021. Therefore, this study further utilized three performance metrics for the years 2019 and 2021, along with the average scores from 2017 to 2021, to analyze the weights among each of the regions: the African region and the Eastern Mediterranean Region (EMR). 2.2 Quantifying regional disparities in health security priorities in EU region: An entropy-based assessment of factor significance The Entropy Method, grounded in information theory, 40 objectively assigns criterion weights in multi-criteria decision-making (MCDM) by analyzing data variability. 22 This approach quantifies the informational value of each criterion through entropy calculations, prioritizing those with higher divergence (i.e., lower entropy) to minimize subjective bias. 41 By algorithmically deriving weights from empirical data distributions, it ensures transparency, scalability, and adaptability, making it ideal for diverse fields. 5 , 6 , 22 , 41 While reliant on robust data quality, its mathematical rigor supports reproducible outcomes and complements expert judgment, bridging data-driven objectivity with domain-specific insights in complex decision scenarios. 5 , 6 Following the framework outlined in, 5 , 6 the process begins by constructing a decision matrix (G), as shown in Equation (1) , which organizes health security ratings for each country and indicator. Here, G represents the decision matrix, where g ij denotes the health security rating of country i for indicator j. In this matrix, m denotes the number of nations analyzed, while n corresponds to the quantity of health security indicators assessed. (1) G = g 11 g 12 … g 1 n … . . . … … g m 1 g m 2 … g mn , Next, to ensure comparability across heterogeneous criteria, each g ij element is normalized using Equation (2) . (2) n ij = g ij ∑ i = 1 m g ij . Following this, Equation (3) calculates the entropy value Ent j for each indicator j, quantifying the uncertainty or spread in the dataset. (3) Ent j = − ( 1 ln ( m ) ) ∑ i = 1 m n ij ∗ ln n ij , ∀ j . The final step involves calculating divergence and assigning criterion weights. Divergence, derived as 1 − Ent j , reflects the informational significance of each indicator. This value is normalized to produce the final weights W j , as formalized in Equation (4) . Criteria with higher variability—indicating greater discriminatory power—receive elevated weights, prioritizing them in the decision-making process. (4) W j = 1 − Ent j ∑ j = 1 n 1 − Ent j , ∀ j . For the EU-2019, as an example, a decision matrix ( Table 1 ) was structured with performance scores for 27 EU countries across predefined HeS indicators. Next, normalization using Equation (2) standardized the data ( Table 2 ), scaling values relative to column sums. Entropy values ( Equation 3 , Table 3 ) were then computed to quantify each indicator’s informational uncertainty. Divergence scores, calculated via Equation (4) , translated entropy into relative importance, with higher divergence indicating greater criterion utility. Finally, normalized divergence values yielded definitive weights, prioritizing indicators with higher variability to ensure data-driven, unbiased policy recommendations. Detailed calculations for this step are available in the supplementary file (Step 2- sheet 2.1(a,b,c)). 42 Table 1. HeS decision matrix for EU (2019). N Country Prevention Detection and reporting Rapid response Health system Compliance with norms Risk environment 1 Austria 53.3 38.8 47.9 54 63.9 86.5 2 Belgium 57.5 52.9 57.5 64.3 60.6 78.4 3 Bulgaria 66.7 61.7 49 58.3 69.4 63.5 4 Croatia 51.3 37.8 37 51.4 55 66.2 . . . . . . . . . . . . . . . . . . . . . . . . 27 Sweden 80.6 64.6 46.1 53.6 69.4 83.8 Table 2. Standardized HeS scores for EU (2019). N Country Prevention Detection and reporting Rapid response Health system Compliance with norms Risk environment 1 Austria 0.038 0.030 0.033 0.038 0.038 0.043 2 Belgium 0.041 0.040 0.039 0.046 0.036 0.039 3 Bulgaria 0.047 0.047 0.034 0.041 0.042 0.032 4 Croatia 0.036 0.029 0.025 0.036 0.033 0.033 . . . . . . . . . . . . . . . . . . . . . . . . 27 Sweden 0.01483 0.00940 0.01342 0.00989 0.01897 0.01441 Table 3. Entropy values, degrees of divergence and final weights of for HeS Indicators. Process N Country Prevention Detection and reporting Rapid response Health system Compliance with norms Risk environment n ij *ln ( n ij ) 1 Austria -0.124 -0.104 -0.112 -0.125 -0.125 -0.136 2 Belgium -0.131 -0.130 -0.127 -0.141 -0.121 -0.127 3 Bulgaria -0.145 -0.144 -0.114 -0.132 -0.132 -0.110 4 Croatia -0.121 -0.102 -0.093 -0.121 -0.113 -0.113 . . . . . . . . . . . . . . . . . . . . . . . . 27 Sweden -0.164 -0.149 -0.109 -0.124 -0.132 -0.133 Sum -3.27368 -3.24666 -3.26812 -3.27420 -3.29006 -3.29021 Entj 0.99328 0.98508 0.99159 0.99344 0.99825 0.99829 1-Entj 0.007 0.015 0.008 0.007 0.002 0.002 Wj 0.168 0.372 0.210 0.164 0.044 0.043 The iterative procedure of the entropy method was replicated 3 times for 2019, 2021, and the aggregated 2017–2019 timeframe. 2.3 Quantifying regional disparities in health security priorities in the African and Eastern mediterranean regions To compare the entropy weighting results with previous regional research from the African region 5 and the Eastern Mediterranean Region (EMR), 6 the iterative procedure of the entropy method was replicated six additional times (for 2 regions across 3 time periods: 2019, 2021, and the aggregated timeframe of 2017–2019). Detailed calculations for this step are available in the supplementary file (Step 2 –Sheets 2.2, and 2.3). 42 2.4 Evaluating and prioritizing countries using Entropy-CoCoSo model This study applies the Combined Compromise Solution (CoCoSo) framework to evaluate health security performance across EU. As a robust MCDM technique, the CoCoSo method 43 integrates the advantages of the simple additive weighting (SAW) model and the exponentially weighted product (EWP) approach. 43 This ranking method is further enhanced by integrating the entropy method, which objectively determines the weights and decision results using the CoCoSo method. 36 The entropy-CoCoSo approach has been widely applied in diverse fields, including engineering sustainability selection 36 and spatial-temporal analysis of business environments. 33 It offers enhanced precision and a finer level of differentiation between alternatives than other MCDM methods. 43 The CoCoSo approach comprises the following four key steps 43 : • Step 1: Formulating DM (G) and its normalized DM (N) These matrices are identical to the existing decision matrices provided in (1) and (5): (5) n ij = { g ij − g j − g j + − g j − , C is a benefit criteria g ij − g j + g j − − g j + , C is a desirable criteria . Here, g ij represents the rating of alternative (Country) i for criterion j, with g j + and g j − corresponding to the highest and lowest criterion-specific scores across all alternatives. • Step 2: Computing the weighted comparability sequences as follows: (6) CS i = ∑ j = 1 n w j n ij , (7) P i = ∑ j = 1 n ( n ij ) w j . Here, the weighting factor ( w j ) represents the priority (Significance ) given to the j-th criterion, while ( n ij ) captures the normalized score of the i-th alternative for the j-th evaluation criterion. • Step 3: Determining priority weights This phase involves deriving three distinct performance metrics through the following formulations: (8) k ia = ( P i + S i ) / ∑ i = 1 m ( P i + S i ) , (9) k ib = ( S i / min S i ) + ( P i / min P i ) , (10) k ic = ( λ ( S i ) + ( 1 − λ ) P i ) ( λ ( max S i ) + ( 1 − λ ) max P i ) . Equation (A) aggregates normalized outcomes from additive and multiplicative weighting techniques. Equation (B) quantifies comparative deviations of each method’s results relative to their minima. Equation (C) harmonizes the trade-off between additive (Si) and multiplicative (Pi) dominance using a tuning coefficient α, which spans 0 to 1. This coefficient enables analysts to calibrate the dominance of either method: α = 0.5 ensures parity, while deviations skew emphasis toward one technique. • Step 4: Generating unified composite score index and prioritization A consolidated performance index is derived via Equation (11) . Entities are then sequenced hierarchically by descending index magnitude: (11) C i = ( ( k ia + k ib + k ic ) 1 3 ) + ( ( k ia + k ib + k ic ) 3 ) . This research utilized a hybrid entropy-CoCoSo technique to assess and hierarchically classify nations according to their health security efficacy over three distinct timeframes (2017–2019, 2019, and 2021). The methodology evaluated national readiness and resilience in mitigating global health risks. For the 2019 EU regional analysis, a focused dataset encompassing 27 countries ( Table 1 ) underwent normalization via Equation 5 ( Table 4 ). Subsequent computational stages involved deriving weighted aggregate scores ( Equations 6 – 7 , Tables 5 – 6 ) and synthesizing multi-dimensional appraisal metrics ( Equations 8 – 10 , Table 7 ). Composite indices derived from Equation 11 facilitated the hierarchical ordering of nations based on their overall performance scores. Detailed calculations for this algorithm are available in the supplementary file (Step 3-Sheet 3.1). 42 Table 4. Normalized HeS scores for EU Countries based on (2019-dataset). N Country Prevention Detection and reporting Rapid response Health system Compliance with norms Risk environment 1 Austria 0.45726 0.35902 0.32800 0.67955 0.45490 1.00000 2 Belgium 0.54076 0.62406 0.52000 0.91364 0.32549 0.74286 3 Bulgaria 0.72366 0.78947 0.35000 0.77727 0.67059 0.26984 4 Croatia 0.41750 0.34023 0.11000 0.62045 0.10588 0.35556 . . . . . . . . . . . . . . . . . . . . . . . . 27 Sweden 1.00000 0.84398 0.29200 0.67045 0.67059 0.91429 Table 5. Weighted comparability sequence scores (CSi). N Country Prevention Detection and reporting Rapid response Health system Compliance with norms Risk environment CSi 1 Austria 0.077 0.134 0.069 0.111 0.020 0.043 0.453 2 Belgium 0.091 0.232 0.109 0.150 0.014 0.032 0.628 3 Bulgaria 0.121 0.294 0.073 0.127 0.029 0.011 0.657 4 Croatia 0.070 0.127 0.023 0.102 0.005 0.015 0.341 . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Sweden 0.168 0.314 0.061 0.110 0.029 0.039 0.721 Table 6. Squared weighted comparability sequence scores (Pi). N Country Prevention Detection and reporting Rapid response Health system Compliance with norms Risk environment Pi 1 Austria 0.877 0.683 0.791 0.939 0.966 1.000 5.256 2 Belgium 0.902 0.839 0.872 0.985 0.952 0.987 5.538 3 Bulgaria 0.947 0.916 0.802 0.960 0.983 0.946 5.553 4 Croatia 0.864 0.669 0.629 0.925 0.906 0.957 4.951 . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Sweden 1.000 0.939 0.772 0.937 0.983 0.996 5.627 Table 7. Final composite scores (ci) and ranks (Ri) of countries. N Country k ia k ib k ic Ci Ri 1 Austria 0.038 15.125 0.851 6.123 17 2 Belgium 0.041 20.247 0.919 7.979 10 3 Bulgaria 0.041 21.090 0.926 8.280 8 4 Croatia 0.035 11.798 0.789 4.894 23 . . . . . . . . . . . . . . . . . . . . . 27 Sweden 0.042 22.966 0.946 8.953 6 2.5 K-Means clustering for EU health security rankings K-means clustering, a sophisticated method for partitioning datasets into distinct groups, 28 was employed to classify countries in Africa, 5 Western Asia, 10 and the Eastern Mediterranean Region 6 based on HeS scores of countries. This technique assigns alternatives to clusters based on similarity, minimizes within-cluster variance, and ensures that alternatives within a group exhibit comparable HeS characteristics. K-means is particularly effective for large datasets and yields clear and interpretable results, making it ideal for this analysis. 29 Given a composite scores vector X = { X 1 , X 2 , … . , X m } , where each X i ∈R represents a data point (composite score) for an EU country under study, and m is the total number of such scores, the K-means clustering algorithm aims to partition these data points into k clusters denoted as L = { L 1 , L 2 , … . . , L k } . The K-means clustering methodology unfolds through sequential phases 44 , 45 : Step 1 : Initialize k cluster centroids ( μ i ( 0 ) , μ i ( 0 ) , … . , μ k ( 0 ) ) are randomly selected from the dataset. Here, each μ k represents the centroid (i.e., the mean) of cluster L k . Step 2 : Assign each data point X i to the cluster L k with the nearest centroid. This assignment is based on the squared Euclidean distance: (12) L k ( t ) = { X i : ‖ X i − μ k ( t ) ‖ 2 ≤ ‖ X i − μ j ( t ) ‖ 2 , ∀ j = { 1 , 2 , … , K } Step 3: Recalculate the centroid of each cluster as the mean of all points assigned to it: (13) μ k ( t + 1 ) = 1 | L k ( t ) | ∑ x i ϵ L k ( t ) X i Step 4: Repeat Steps 2 and 3 iteratively until convergence. Convergence is typically defined by one of the following criteria: (1) cluster assignments do not change between iterations, (2) centroids move less than a predefined threshold, or (3) a maximum number of iterations is reached. At each iteration, the algorithm seeks to minimize the intra-cluster variance, denoted by the objective function J, which encourages the formation of compact and well-separated clusters: (14) J = argmin ∑ i = 1 , ( x i ϵ L k ) k ∑ ‖ X i − μ k ‖ 2 Here, ‖ X i − μ k ‖ 2 is the squared Euclidean distance between a point X i and the centroid of its assigned cluster μ k . In this study, we employed the Elbow Method to determine the optimal number of clusters, using the Within-Cluster Sum of Squares (WCSS) as the evaluation criterion. The “elbow” point on the WCSS curve represents the point at which the rate of decrease significantly changes, indicating diminishing returns from adding additional clusters. As illustrated in Figure 1 , there was a substantial reduction in WCSS when increasing from one to two clusters, followed by a smaller yet noticeable decrease from two to three clusters. The decrease from three to four clusters was even less pronounced, with subsequent reductions continuing at a gradual pace. Consequently, the elbow point was identified between two and three clusters. Based on this analysis, we selected three clusters as the optimal number for our study, achieving a balance between minimized WCSS and diminishing marginal improvement. Figure 1. Elbow Method results for determining the optimal number of clusters. The plot displays the relationship between the number of clusters (k) and the within-cluster sum of squares (WCSS). In terms of performance levels, Cluster 1 represented ‘High’ performance, Cluster 2 ‘Intermediate’, and Cluster 3 ‘Dangerous’, enabling a nuanced evaluation of each country's regional standing. The clustering method was applied 3 times to cluster EU countries based on the results of the ranking method across the three datasets. Detailed calculations for this step are available in the supplementary file (sheets: 4.1, 4.2). 42 3. Results 3.1 Entropy-Driven assessment of HeS priorities The calculated entropy weights of HeS indicators for EU countries, alongside comparative weights for the African Region and Eastern Mediterranean Region (EMR), are presented in Table 8 . Table 8 and Figure 2 offer critical insights into the prioritization of HeS indicators across regions. Figure 2 illustrates the weight distribution of HeS-related dimensions, comparing the EU, African Region, and EMR over three timeframes: 2019, 2021, and the aggregated period (2017–2021). Additionally, Table 9 provides tabular representations of dynamic prioritization trends, highlighting changes in indicator weights between 2019 and 2021. Table 8. Regional prioritization of HeS dimensions: EU vs. EMR vs. African Region. Data Region Prevention Detection and reporting Rapid response Health system Compliance with norms Risk environment 2019 European Union 0.168 0.372 0.210 0.164 0.044 0.043 2021 0.162 0.360 0.207 0.182 0.044 0.045 Aggregated 0.168 0.371 0.200 0.176 0.040 0.044 2019 African Region 0.330 0.326 0.060 0.172 0.057 0.055 2021 0.352 0.294 0.072 0.169 0.051 0.063 Aggregated 0.325 0.322 0.061 0.175 0.056 0.061 2019 Eastern Mediterranean 0.209 0.217 0.098 0.322 0.060 0.095 2021 0.259 0.221 0.096 0.261 0.063 0.099 Aggregated 0.228 0.220 0.092 0.298 0.063 0.099 Figure 2. Regional prioritization of HeS dimensions: EU vs. EMR vs. African Region. This figure illustrates the entropy-weighted prioritization of health security dimensions across EU countries, the Eastern Mediterranean Region (EMR), and the African Region (2017–2021). The figure compares the relative weights of six Global Health Security Index (GHSI) indicators—Prevention, Detection & Reporting, Rapid Response, Health System, Compliance with Norms, and Risk Environment—across regions and timeframes. Table 9. Temporal shifts in HeS indicator weights: EU, African Region, and EMR (2019 vs. 2021). Region Prevention Detection and reporting Rapid response Health system Compliance with norms Risk environment European Union -0.005 -0.013 -0.003 0.018 0.000 0.003 African R 0.023 -0.032 0.011 -0.004 -0.006 0.008 Eastern Mediterranean 0.051 0.004 -0.003 -0.060 0.004 0.004 3.2 EU health security rankings and cluster-based interventions The HeS performance assessments (Ci) and corresponding rankings (Ri) for EU countries, derived from the Entropy-CoCoSo method, are displayed in Table 10 . These evaluations cover three time frames: 2019, 2021, and the combined period of 2017–2021. Each nation is assigned to one of three performance categories (Si), ranging from S1 (indicating superior performance) to S3 (signifying a critical situation ‘Dangerous’). These tiers reflect varying degrees of outcomes, from optimal to severely inadequate. The table also highlights changes in ranking positions and tier classifications between 2019 and 2021, revealing trends of progress or regression among these nations. A visual representation of the ranking trajectories is provided in Figure 3 , while Figure 4 showcases the distribution of cluster tiers across the examined states. Table 10. Entropy-CoCoSo HeS Scores (C i ), Rankings (R i ), and Cluster Assignments (S i ) for EU Countries. N State Case 1-2019 Case 2-2021 Case 3-The whole period (2017-2021) Shifts Ci Ri Si Ci Ri Si Ci Ri Si Ri Si A-1 Austria 6.123 17 2 3.910 15 2 4.969 15 2 2 0 A-2 Belgium 7.979 10 1 4.637 12 1 6.210 10 1 -2 0 A-3 Bulgaria 8.280 8 1 4.886 10 1 6.502 9 1 -2 0 A-4 Croatia 4.894 23 2 3.002 23 2 3.881 23 2 0 0 A-5 Cyprus 2.280 26 3 1.864 26 3 1.933 26 3 0 0 A-6 Czech Republic 5.963 18 2 3.419 22 2 4.711 19 2 -4 0 A-7 Denmark 9.581 3 1 5.489 5 1 7.426 3 1 -2 0 A-8 Estonia 6.157 16 2 3.890 16 2 4.932 16 2 0 0 A-9 Finland 10.442 1 1 6.275 1 1 8.262 1 1 0 0 A-10 France 7.745 11 1 4.740 11 1 6.189 11 1 0 0 A-11 Germany 9.250 5 1 5.571 3 1 7.319 4 1 2 0 A-12 Greece 5.617 21 2 3.473 21 2 4.483 21 2 0 0 A-13 Hungary 6.169 15 2 3.776 18 2 4.887 17 2 -3 0 A-14 Ireland 6.299 14 2 4.037 13 2 5.129 14 2 1 0 A-15 Italy 5.841 19 2 3.622 20 2 4.644 20 2 -1 0 A-16 Latvia 8.598 7 1 5.502 4 1 7.042 6 1 3 0 A-17 Lithuania 6.912 13 2 4.944 9 1 5.969 12 1 4 1 A-18 Luxembourg 3.935 24 3 2.523 25 3 3.148 24 3 -1 0 A-19 Malta 1.025 27 3 1.109 27 3 1.085 27 3 0 0 A-20 Netherlands 9.516 4 1 5.360 6 1 7.289 5 1 -2 0 A-21 Poland 5.633 20 2 3.951 14 2 4.795 18 2 6 0 A-22 Portugal 6.984 12 2 3.801 17 2 5.231 13 2 -5 0 A-23 Romania 3.736 25 3 2.587 24 3 3.117 25 3 1 0 A-24 Slovakia 5.083 22 2 3.767 19 2 4.473 22 2 3 0 A-25 Slovenia 9.853 2 1 6.140 2 1 7.965 2 1 0 0 A-26 Spain 8.116 9 1 5.164 8 1 6.603 8 1 1 0 A-27 Sweden 8.953 6 1 5.236 7 1 6.999 7 1 -1 0 Figure 3. Entropy-CoCoSo-based ranking results and ranking shifts (2019–2021). This bar chart illustrates the Health Security Preparedness Rankings of non-EU European countries, encompassing the years 2019, 2021, and the entire period of 2017-2021. Countries are ranked based on their Composite CoCoSo scores, with higher values indicating superior performance. It also reflects temporal shifts in health security capabilities. Figure 4. Health security performance clustering results of EU countries. This figure illustrates the K-Means Clustering results of EU countries, encompassing the years 2019, 2021, and the entire period of 2017-2021. Countries are grouped into three clusters based on their health security profiles, with Cluster 1 representing exemplary performance and Cluster 3 representing critical performance. The figure also reveals patterns of similarity and dissimilarity among countries in terms of their health security capabilities. 3.3 Comparison of Entropy-CoCoSo with other Entropy-based MCDM methods This study evaluated the efficacy of the Entropy-CoCoSo (E-CoCoSo) method in comparison to four other entropy-based methods—Entropy-TOPSIS, Entropy-EDAS, Entropy-WASPAS, and Entropy-VIKOR—across three distinct periods (2019, 2021, and 2017–2021). Figure 5.a , 5.b , and 5.c depict the rankings of the 27 EU countries as alternatives for each scenario, while Table 11 presents the corresponding Spearman’s rank correlation coefficients to validate the consistency of the methods. Figure 5.a. Rankings of 27 EU countries using Entropy-CoCoSo, TOPSIS, EDAS, WASPAS, and VIKOR methods for the year 2019. This Chart demonstrate consistency across different multi-criteria decision-making approaches in evaluating health security performance. It compares the EU country rankings using Entropy-CoCoSo, Entropy-TOPSIS, Entropy-EDAS, Entropy-WASPAS, and Entropy-VIKOR methods for 2019. Figure 5.b. Rankings of 27 EU countries using Entropy-CoCoSo, TOPSIS, EDAS, WASPAS, and VIKOR methods for the year 2021. This Chart demonstrate consistency across different multi-criteria decision-making approaches in evaluating health security performance. It compares the EU country rankings using Entropy-CoCoSo, Entropy-TOPSIS, Entropy-EDAS, Entropy-WASPAS, and Entropy-VIKOR methods for 2021. Figure 5.c. Rankings of 27 EU countries using Entropy-CoCoSo, TOPSIS, EDAS, WASPAS, and VIKOR methods for the period 2017-2021. This chart reflects the consistency among different multi-criteria decision-making frameworks in assessing health security performance. It provides a comparative analysis of EU country rankings using the Entropy-CoCoSo, Entropy-TOPSIS, Entropy-EDAS, Entropy-WASPAS, and Entropy-VIKOR methods over the specified period. Note: A1 – Austria, A2 – Belgium, A3 – Bulgaria, A4 – Croatia, A5 – Cyprus, A6 – Czech Republic, A7 – Denmark, A8 – Estonia, A9 – Finland, A10 – France, A11 – Germany, A12 – Greece, A13 – Hungary, A14 – Ireland, A15 – Italy, A16 – Latvia, A17 – Lithuania, A18 – Luxembourg, A19 – Malta, A20 – Netherlands, A21 – Poland, A22 – Portugal, A23 – Romania, A24 – Slovakia, A25 – Slovenia, A26 – Spain, and A27 – Sweden. Table 11. Spearman’s ranking coefficients of correlation (Average). Spearman’s rho Entropy-CoCoSo Entropy-TOPSIS Entropy-EDAS Entropy WASPAS Entropy-VIKOR Entropy-CoCoSo 1 0.963777 0.9866 0.9843305 0.96622 Entropy -TOPSIS 0.963777 1 0.9827 0.986569 0.99451 Entropy –EDAS 0.986569 0.982702 1 0.9953195 0.98107 Entropy WASPAS 0.98433 0.986569 0.9953 1 0.98331 Entropy -VIKOR 0.966219 0.994505 0.9811 0.983313 1 4. Discussion 4.1 Comparative weighting of health security domains: EU, Africa, and EMR perspectives The model's first step establishes a hierarchy of importance for health security indicators, aiding decision-makers in resource allocation and prioritization. Weighted indicators enable thorough analysis of complex systems, considering each indicator's impact on outcomes. Longitudinal analysis across 2019, 2021, and 2017-2021 provides insights into evolving health security priorities, helping identify trends, shifts, and emerging challenges. Regional comparisons (EU, Africa, and EMR) highlight disparities in priorities and resource allocation, informing targeted global health initiatives. These evaluations promote international collaboration and knowledge sharing, with successful regions serving as models. Recognizing regional differences allows for tailored, context-specific approaches to health security. Dynamic analysis of indicator weights between 2019 and 2021, compared across regions and sub-regions, offers a comprehensive understanding of evolving health security priorities. Policymakers can use these insights to identify trends, assess intervention effectiveness, and design responsive strategies. 4.1.1 The entropy-derived weights for EU countries The entropy-derived weights reveal that Detection & Reporting (0.36–0.37) and Rapid Response (0.20–0.21) are the most influential criteria in differentiating health security performance across EU countries, indicating significant disparities in surveillance capabilities and emergency responsiveness between states. The slight decline in Detection & Reporting’s weight by 2021 (0.372 to 0.360) suggests reduced variability in this domain, potentially reflecting pandemic-driven convergence in surveillance systems. This convergence may be attributed to the EU's efforts to harmonize data collection and reporting through initiatives like the European Surveillance System (TESSy). In contrast, the rising weight of health systems (0.164 to 0.182) signals growing divergence in healthcare infrastructure resilience post-2019, likely exacerbated by uneven pandemic recovery efforts. This divergence is concerning, as it suggests that some member states are falling behind in their ability to provide essential healthcare services during emergencies, potentially due to underinvestment in healthcare infrastructure and shortages of healthcare professionals. 46 Policymakers should prioritize investments in strengthening healthcare systems in the most vulnerable member states to ensure equitable access to care during future health crises. 47 Prevention’s stable weight (~0.16) implies moderate but consistent differentiation, while compliance with norms and risk environments—persistently low weights (~0.04)—highlights their negligible role in distinguishing performance, either due to uniform underperformance or insufficient data variability. These weights prioritize addressing disparities in high-impact areas (detection & reporting, rapid response) over uniformly improving low-weight domains. Policymakers should thus focus on harmonizing detection capacities and emergency coordination mechanisms to reduce inter-country gaps while investigating systemic weaknesses in compliance and risk environments that the model currently overlooks. The aggregated weights’ stability underscores the enduring centrality of detection & reporting as a benchmark, urging targeted investments to sustain its discriminative power in EU health security evaluations. 4.1.2 Comparative analysis of EU and African health security priorities The entropy weights highlight stark contrasts in the factors driving health security differentiation between the EU and Africa. In Africa, prevention (0.325–0.352) and detection & reporting (0.294–0.326) dominate as the most influential criteria, reflecting significant disparities in preemptive measures and surveillance capabilities across African nations. 5 By contrast, the EU’s model prioritizes Detection & Reporting (0.360–0.372) and Rapid Response (0.20–0.21), underscoring variability in crisis coordination and real-time surveillance as key differentiators. Notably, Rapid Response is far less impactful in Africa (0.060–0.072 vs. the EU’s ~0.20), suggesting emergency mobilization systems are either underdeveloped or uniformly weak across the continent. Temporal trends also diverge. Africa’s prevention weight increased by 2021 (0.330 → 0.352), likely driven by pandemic-era investments in preventive care, while its detection weight declined (0.326 → 0.294), signaling reduced variability in surveillance systems. Conversely, the EU saw rising health system weights (0.164 → 0.182), emphasizing post-2019 disparities in healthcare resilience but stagnant prevention influence (~0.16). Both regions show minimal differentiation from compliance with norms and risk environments (weights ~0.04–0.06), though Africa’s slightly higher values hint at marginally greater variability in regulatory adherence or environmental risks. The contrasting health security priorities between the EU and Africa have important implications for potential collaboration. The EU could provide technical assistance and financial support to help African countries strengthen their surveillance systems and improve their capacity for rapid response. At the same time, African countries could share their expertise in managing infectious diseases and implementing community-based prevention programs. By working together, the EU and Africa can enhance global health security. 4.1.3 Comparative analysis with the Eastern Mediterranean Region (EMR) The EMR’s entropy weights reveal Health System (0.261–0.322) as the dominant driver of health security differentiation, contrasting sharply with the EU’s focus on Detection & Reporting and Africa’s emphasis on Prevention. While EMR’s health system weight declined post-2019 (0.322 → 0.261), due to pandemic strain on infrastructure, 6 prevention gained prominence (0.209 → 0.259), reflecting growing disparities in preemptive measures like vaccine equity or sanitation access. EMR’s Detection & Reporting weights (0.217–0.221) are lower than the EU’s (0.36–0.37) but higher than Africa’s (0.29–0.33), suggesting moderate variability in surveillance systems. Rapid Response (0.092–0.098) remains a minor differentiator, similar to Africa but far below the EU, indicating widespread emergency coordination gaps. Compliance with Norms and Risk Environment (~0.06–0.10) holds marginally higher weights than in the EU or Africa, hinting at slightly greater variability in regulatory adherence or climate-linked health risks. 4.1.4 EU Health security priorities: Evolution and regional divergence in a post-pandemic context (2019–2021) The EU’s dynamic prioritization of health security indicators between 2019 and 2021 reveals a nuanced evolution shaped by the COVID-19 pandemic. Detection and Reporting (-0.013) and Prevention (-0.005) saw modest declines in weight, potentially reflecting a pandemic-induced homogenization of surveillance and preventive measures across member states as they adopted standardized responses. However, this apparent convergence should be interpreted cautiously, as it may mask underlying disparities in the effectiveness of these measures. In contrast, Health System (+0.018) gained importance, signaling growing post-2019 disparities in healthcare resilience, likely exacerbated by uneven national recovery efforts and pre-existing vulnerabilities in certain member states. 46 Compared to Africa, where prevention surged (+0.023) and detection plummeted (-0.032), the EU’s prioritization diverges sharply. This divergence underscores the contrasting realities and strategic choices facing the two regions: Africa's focus shifted toward preemptive measures, likely driven by the need to compensate for fragmented and under-resourced surveillance systems, 48 , 49 while the EU, with its stronger baseline detection capabilities, experienced a relative decline in the differentiating power of those systems. The Eastern Mediterranean Region (EMR) diverges further, with Prevention (+0.051) rising steeply and Health System (-0.060) collapsing as a differentiator. This dramatic shift likely reflects the severe strain placed on healthcare infrastructure in the EMR by the pandemic, coupled with a strategic emphasis on prevention in resource-constrained settings. Unlike the EU’s stable compliance, both Africa (-0.006) and EMR (+0.004) show minimal regulatory prioritization, highlighting the EU’s relative institutional maturity, even though all regions share persistent gaps in Rapid Response (-0.003 to -0.007). This shared weakness in rapid response capabilities underscores the urgent need for improved cross-border coordination and resource mobilization to effectively address future health emergencies. 5 , 6 4.2 Discussion of HeS performance ranking results The Health Security Index (HSI) rankings for EU countries revealed significant variability between 2019 and 2021. Nine EU nations (33.33%)—including Austria, Germany, Ireland, Latvia, Lithuania, Poland, Romania, Slovakia, and Spain—exhibited improved rank values, reflecting tangible progress in specific health security measures or a more effective overall pandemic response. Conversely, the rankings remained unchanged in eight countries (29%), such as Croatia, Cyprus, Estonia, Finland, France, Greece, Malta, and Slovenia, suggesting either a stable baseline performance or a balancing of gains and losses across different HSI indicators. However, a decline was observed in ten EU countries (37%), including Belgium, Bulgaria, Czech Republic, Denmark, Hungary, Italy, Luxembourg, Netherlands, Portugal, and Sweden, signaling potential vulnerabilities that were exposed or exacerbated during the pandemic and warrant further investigation to identify specific areas for improvement in their health security systems ( Figure 3 ). These shifts in rankings underscore the dynamic nature of health security and the importance of continuous monitoring and adaptation. Poland's significant improvement, surging six ranks (20→14), demonstrates the potential for rapid progress through targeted investments and policy reforms, such as increased funding for detection and reporting infrastructure or improved health system capabilities. 9 The advancements made by Lithuania (+4) and Latvia (+3), along with moderate gains in Central Europe (Slovakia +3, Austria/Germany +2) and Western nations (Ireland/Spain +1), underscore the diverse approaches to health security within the EU, reflecting varying national priorities, resource allocations, and pre-existing strengths and weaknesses. While Finland and Slovenia maintained their leading positions in EU health security rankings from 2019 to 2021, suggesting robust and resilient health security systems, other countries experienced notable shifts that highlight the need for vigilance. Denmark's fall from 3rd to 5th place, potentially due to weakened prevention and response measures as resources were diverted to managing the acute phase of the pandemic, illustrates the importance of maintaining robust health security systems across all domains, even during times of crisis. 9 Similarly, the Netherlands' critical declines in various areas, including trade and travel restrictions (scores: 100→0), likely reflecting a shift in policy toward less restrictive measures, and zoonotic management, expose potential vulnerabilities in surveillance and mitigation strategies that require careful re-evaluation. The consistently low rankings of Luxembourg, Romania, Cyprus, and Malta reveal systemic challenges in their health security frameworks. These countries scored poorly in critical areas such as biosecurity, laboratory systems, and emergency financing, highlighting the need for comprehensive reforms. However, each of these nations also demonstrated niche strengths, such as Romania's zoonotic surveillance and Malta's workforce strategy, which could serve as foundations for improvement. The wide variation in scores within countries reflects uneven priorities or resource allocation in health security measures, rather than uniform strengths or weaknesses. While shared strengths in urbanization and road networks may facilitate rapid response in some areas, persistent gaps in biosecurity, laboratory capacity, and emergency preparedness require targeted interventions and tailored strategies to address specific national needs. Smaller nations like Luxembourg and Malta face unique resource constraints, necessitating innovative approaches and potential collaboration with larger member states to enhance their health security capabilities. Addressing these deficits while leveraging existing strengths is crucial for improving resilience against future health crises across the EU. The diverse performance of EU countries in the Health Security Index underscores the need for continued collaboration, knowledge sharing of best practices, and targeted improvements to enhance overall health security within the region. This includes strengthening cross-border coordination, investing in research and development, and promoting a culture of preparedness at all levels of society. 4.3 Comparison of E-CoCoSo with other entropy-based MCDM methods Overall, the E-CoCoSo method exhibited excellent concordance with the other approaches ( Figure 5.a , 5.b , and 5.c ), particularly with E-EDAS (ρ = 0.9866) and E-WASPAS (ρ = 0.9843) ( Table 11 ). The correlation with E-TOPSIS was also notably strong (ρ = 0.9638), whereas the lowest—yet still high—correlation was observed with E-VIKOR (ρ = 0.9662). A detailed comparison of E-CoCoSo and E-EDAS across the three cases revealed a remarkable level of consistency and similarity. The rankings produced by E-CoCoSo closely mirrored those of E-EDAS, especially among the top-performing alternatives such as A9, A25, A7, and A11. Minor differences appeared mainly among the mid- and lower-ranked alternatives; however, these variations were minimal and did not substantially impact the overall ranking trends. This high level of alignment indicates that E-CoCoSo provides a comparable degree of reliability and robustness to the well-established E-EDAS method across different evaluation periods. Similarly, the comparison between E-CoCoSo and Entropy-WASPAS confirms a very high degree of agreement, as reflected by a Spearman’s correlation coefficient of ρ = 0.9843. Both methods consistently ranked the leading alternatives across all three cases, with particularly close alignment in the first and third cases. Although minor differences were again noted among the lower-ranked alternatives, the overall ranking structure remained stable, reaffirming the strong consistency of the E-CoCoSo method relative to Entropy-WASPAS. Although the correlation between E-CoCoSo and E-VIKOR was slightly lower, it remained strong, suggesting that while both methods generally agree, some differences in ranking outcomes exist. These discrepancies can be attributed to variations in the underlying algorithmic structures and aggregation techniques employed by each method, highlighting that each approach retains unique characteristics. Furthermore, among all method pairs, the strongest overall correlation was observed between Entropy-EDAS and Entropy-WASPAS (ρ = 0.9953), underscoring their extremely similar outputs. Entropy-TOPSIS and Entropy-VIKOR also exhibited a near-perfect correlation (ρ = 0.9945), emphasizing their strong mutual consistency. Taken together, the consistently high Spearman’s coefficients across all comparisons confirm that entropy-based MCDM methods, including E-CoCoSo, produce highly comparable and reliable decision-making outcomes, thus reinforcing the validity of E-CoCoSo as a robust tool for multi-criteria evaluations. 4.4 Health security performance: Insights and patterns across three clusters Based on the final Entropy-CoCoSo assessment scores, EU countries were grouped into three health security performance clusters for both the sub-assessment years and the entire analyzed period ( Table 12 , Figure 6 ). These clusters range from “High” to “Dangerous,” providing a nuanced picture of health security preparedness across the EU. Table 12. Health security performance clusters of the EU-27 countries. Cluster (Level) Case - 2019 Case - 2021 The whole period (2017-2021) 1 (High) Belgium, Bulgaria, Denmark, Finland, France, Germany, Latvia, Netherlands, Slovenia, Spain, and Sweden Belgium, Bulgaria, Denmark, Finland, France, Germany, Latvia, Netherlands, Slovenia, Spain, Sweden, and Lithuania Belgium, Bulgaria, Denmark, Finland, France, Germany, Latvia, Netherlands, Slovenia, Spain, Sweden, and Lithuania 2 (Intermediate) Austria, Croatia, Czech Republic, Estonia, Greece, Hungary, Ireland, Italy, Lithuania, Poland, Portugal, and Slovakia Austria, Croatia, Czech Republic, Estonia, Greece, Hungary, Ireland, Italy, Poland, Portugal, and Slovakia Austria, Croatia, Czech Republic, Estonia, Greece, Hungary, Ireland, Italy, Poland, Portugal, and Slovakia 3 (Dangerous) Cyprus, Luxembourg, Malta, and Romania. Cyprus, Luxembourg, Malta, and Romania Cyprus, Luxembourg, Malta, and Romania Figure 6. Average health security performance scores across the clusters. This figure compares the mean scores of six HeS sub-indicators for each cluster (High, Intermediate, and Dangerous). The bar chart contrasts the High, Intermediate, and Dangerous tiers across multiple indicators, highlighting strengths and weaknesses in regional preparedness. Scores range from 0 to 100, with higher values indicating stronger performance. The visualization emphasizes disparities in HeS capabilities across clusters. Source: Authors’ analysis based on GHSI 2021 data. 4.4.1 Bridging gaps in EU health security: Strategic interventions for cluster-specific risks Clustering is crucial for revealing patterns in data, enabling targeted strategies and informed decisions. 50 – 53 An analysis of EU health security scores ( Figure 6 ) reveals stark disparities across clusters, underscoring the urgent need for targeted, collaborative interventions. In prevention, Cluster 1 leads significantly in antimicrobial resistance management (AMR: 82.98), while Cluster 3 (Cyprus, Luxembourg, Malta, and Romania) trails far behind (68.75), posing a substantial threat to regional biosecurity. Zoonotic disease control followed a similar pattern, with Cluster 1 performing the strongest (42.82) and Cluster 3 critically weak (23.58), exposing vulnerabilities to pandemic preparedness. Biosecurity scores were similarly stratified, with Cluster 1 achieving 56.23 and Cluster 3 lagging at 41.00. Biosafety measures revealed an even steeper drop, plunging from Cluster 1 (75.00) to a critical low in Cluster 3 (31.25), highlighting systemic deficiencies in safety management. 9 Dual-use research and the culture of responsible science are nearly absent in Clusters 2 and 3 (0.00 in both), confined almost exclusively to Cluster 1 (13.88), reflecting the widespread neglect of responsible scientific governance. Interestingly, Cluster 2 outperformed slightly in immunization (79.55) compared to Cluster 1 (78.13), despite its broader systemic weaknesses, while Cluster 3 lagged significantly (59.38), suggesting fragmented prioritization of health initiatives. Turning to detection and reporting, Cluster 1 once again dominated across laboratory system strength (82.29), surveillance reporting (65.63), and data transparency (88.75), while Cluster 3 consistently recorded the lowest scores, notably in real-time surveillance (9.38) and laboratory supply chains (0.00), leaving critical gaps in early warning capabilities. Although Cluster 2 performs moderately well in laboratory systems, it dramatically underperforms in laboratory supply chain readiness (4.55), which is a potential bottleneck in emergency response. Emergency response capacities also showed major disparities: Cluster 1 demonstrated stronger preparedness planning (53.12) and operational linkages between public health and security authorities (79.17), whereas Cluster 3 scores were critically low in preparedness (12.50) and response operations (8.33). Despite moderate performance in access to communication infrastructure across all clusters, risk communication effectiveness weakens progressively from Cluster 1 (71.00) to Cluster 3 (51.05). By shifting focus to health systems, contrasts grow starkers. Infection control practices stand out, with both Cluster 1 and Cluster 2 achieving full scores (100.00), while Cluster 3 dramatically underperforms (50.00). However, substantial weaknesses remained in communication with healthcare workers during emergencies, where Cluster 3 scored zero and Cluster 2 trailed significantly (9.09). Medical countermeasure testing struggles with inconsistencies across clusters: Cluster 1 achieves a relatively strong capacity (71.88), Cluster 2 (65.91), and Cluster 3 (62.50) show noticeable declines, reflecting uneven readiness to approve and deploy new treatments during health crises. More critically, Cluster 3 scored zero in both infection control communication and medical countermeasure deployment, a failure that amplifies systemic vulnerabilities and heightens regional risk during public health emergencies. In the international commitment domain, strong scores are observed across clusters for cross-border agreements and international commitments (above 98%), suggesting that regional cooperation frameworks are robust. Nevertheless, financing (Cluster 3:13.56) and participation in international evaluation mechanisms (Cluster 3:0.00 for JEE and PVS) highlight major gaps in sustainable commitment to global health security. Finally, in terms of risk environment factors, all clusters show relatively balanced performance, although Cluster 1 maintains a slightly higher resilience in political security (79.04) and infrastructure adequacy (82.64). Public health vulnerabilities remain concerning across all clusters but are least severe in Cluster 1 (70.95) and most critical in Cluster 3 (67.20). Collectively, these trends revealed a fragmented landscape. High performers, such as Cluster 1, cannot offset systemic failures in Clusters 2 and 3 without EU-wide collaboration. To bridge these gaps, policymakers must prioritize dual-use research governance, laboratory infrastructure upgrades, and standardized crisis drills, leveraging cluster strengths while addressing their unique vulnerabilities. Only through such cohesive action can disparities evolve into unified resilience 4.4.2 Mapping EU health security: Strengths, gaps, and policy implications across clusters The EU’s health security clustering reveals a complex interplay of strengths and vulnerabilities across its member states. High-performing nations—such as Belgium, Bulgaria, Denmark, Finland, France, Germany, Latvia, the Netherlands, Slovenia, Spain, Sweden, and Lithuania—lead in antimicrobial resistance management, laboratory system strength, and emergency preparedness, serving as benchmarks for global health security. However, gaps in biosafety governance, dual-use research policies, and financing persist even among these top performers, underscoring the need for continuous improvement. Notably, the inclusion of countries spanning diverse economic and political backgrounds in this high-performing cluster demonstrates that effective health strategies transcend traditional divides, advocating for cross-regional knowledge-sharing to bolster resilience. Similarly, “intermediate cluster” countries—Austria, Croatia, the Czech Republic, Estonia, Greece, Hungary, Ireland, Italy, Poland, Portugal, and Slovakia—excel in immunization coverage, infection control practices, and cross-border collaboration but face critical risks in laboratory supply chain management, surveillance responsiveness, and emergency communication—patterns that mirror systemic gaps across this cluster. Their moderate performance highlights both opportunities for strategic growth and urgent areas requiring targeted support. Most critically, Cluster 3 (Cyprus, Luxembourg, Malta, and Romania) exemplifies systemic fragility, with severe deficiencies in biosafety, real-time surveillance, medical countermeasure deployment, and dual-use research governance. These vulnerabilities not only jeopardize national preparedness but also create weak links in the EU’s broader health security network. These disparities highlight interconnected challenges: gaps in surveillance infrastructure, health workforce capacity, and medical countermeasure readiness plague multiple clusters, demanding EU-wide standardization and policy coherence. To bridge these gaps, policymakers must prioritize centralized funding mechanisms and mentorship programs that leverage the expertise of high-performing states. 5 , 6 By fostering equity in resource allocation, promoting cross-border technical support, and building a collaborative governance model, the EU can transform its mosaic of strengths and weaknesses into a unified, resilient defence against emerging health threats. 4.5 Implication of study This study offers important insights for policymakers, researchers, and international health organizations by introducing the Entropy-CoCoSo-K-means framework to address limitations in traditional global indices such as the GHSI. This integrated method allows for a more nuanced and context-sensitive assessment of health security across EU member states. The identification of three distinct performance clusters—ranging from high to dangerous—provides a clear roadmap for differentiated, targeted interventions. High-performing countries such as Finland and Germany can serve as regional benchmarks, facilitating cross-border collaboration and capacity building. In contrast, underperforming countries like Cyprus and Malta require urgent investment in laboratory infrastructure, biosafety, and emergency response mechanisms to bridge critical preparedness gaps. The study also reveals how intra-EU disparities in health system resilience, detection capacity, and rapid response reflect both structural inequalities and varied national priorities. These findings emphasize the need for coordinated surveillance harmonization and investment in shared infrastructure, particularly through EU initiatives like HERA and the European Health Union (EHU). Comparative insights from Africa and the Eastern Mediterranean Region (EMR) further underscore the importance of regionalized strategies. While the EU emphasizes detection and response systems, African health systems prioritize prevention, and EMR countries place greater weight on the resilience of their health infrastructure. These contrasts point to mutual learning opportunities. For instance, the EU can support Africa in strengthening surveillance and rapid response capabilities, while benefiting from African and EMR expertise in community-based public health strategies and adaptive responses under resource constraints. Moreover, the observed recalibrations between 2017 and 2021 reflect a broader post-pandemic shift in health security priorities—urging leaders to balance immediate crisis response with long-term structural resilience. By aligning with EU-wide strategies such as the EHU and HERA, this study contributes a robust, metrics-driven model to guide equitable and sustainable health security development across the region. 4.6 Comparative contributions of this study This research significantly advances the field of health security evaluation by overcoming the limitations of mono-dimensional assessments such as the Global Health Security Index (GHSI). 1 Unlike prior studies that rely solely on ranking countries by composite scores, this work employs a more flexible and informative methodology—entropy-based multi-criteria decision-making (MCDM) integrated with K-means clustering—to better account for contextual variability. Compared to existing approaches, 1 , 9 , 16 our framework captures intra-EU disparities more dynamically and identifies previously overlooked vulnerabilities, such as deficiencies in dual-use research governance and biosecurity systems in smaller EU states like Cyprus and Malta. 9 A major contribution of this study lies in the formulation of strategic cross-cluster recommendations to guide EU-wide resilience-building. For Cluster 1 countries (e.g., Finland, Germany), the recommendation is to serve as technical mentors in EU preparedness exercises and lead policy innovation in biosafety and dual-use research governance. For Cluster 2 countries (e.g., Ireland, Slovakia), investment in laboratory infrastructure and crisis communication—possibly supported through EU recovery funding—is critical to addressing performance bottlenecks. 47 Cluster 3 countries (e.g., Cyprus, Malta) must prioritize structural reforms and external support through HERA-led interventions, international partnerships, and regional simulation drills to build core capabilities. These findings support the mission and structure of both HERA and the EHU by offering a data-driven, actionable framework for resource allocation, cross-border solidarity, and long-term preparedness. They also contribute empirical guidance for harmonizing national health systems under supranational governance models. 11 Furthermore, lessons from Africa and EMR contexts reinforce the need for adaptive, bottom-up health strategies. African systems offer replicable community-based prevention models, while EMR countries exemplify resilience under crisis—both of which are instructive for refining EU strategies. 4 , 38 In sum, this study not only fills a methodological gap but also elevates the policy relevance of health security analytics in a post-COVID global landscape. 4.7 Limitations and future work Despite its contributions, this study has several limitations that should be acknowledged. First, the temporal coverage (2017–2021) excludes more recent developments, such as the emergence of Omicron sub variants and key EU reforms like the European Health Data Space (EHDS). 54 Extending the analysis to include post-2021 data would provide a more current picture of evolving health security dynamics. Second, the exclusive use of quantitative metrics may miss qualitative dimensions such as political leadership, institutional trust, and public engagement, which are critical for understanding the real-world effectiveness of health systems. Future research should integrate qualitative methods, including stakeholder interviews or case studies, to complement the MCDM framework. Expanding the Entropy-CoCoSo-K-means approach to subnational or non-EU regions would also help assess its scalability and identify micro-level disparities. Moreover, in-depth studies are needed to explore the causal drivers behind cluster-specific weaknesses—such as differences in governance models, socioeconomic inequality, or healthcare workforce capacity. Longitudinal research tracking targeted interventions in low-performing states like Malta and Cyprus could evaluate whether policy changes lead to measurable improvements. Lastly, deeper inquiry into the root causes of divergent health security priorities between regions like the EU and Africa—whether geopolitical, structural, or fiscal—would further enrich the comparative analysis and enhance the applicability of the framework in broader international contexts. 5. Conclusion This study systematically evaluated health security patterns across the European Union using a hybrid Entropy-CoCoSo and K-means clustering approach, addressing critical gaps in existing supranational analyses. By analyzing six GHSI indicators, the research definitively identified detection and reporting (0.36–0.37 weight) and rapid response (0.20–0.21) as the most critical drivers of disparities in health security performance among EU member states. Temporal trends revealed significant post-pandemic shifts, including growing divergences in health system resilience and persistent stagnation in compliance with international norms, which demand urgent policy attention. The clustering of countries into three distinct tiers (high to dangerous) starkly exposed systemic vulnerabilities in lower-tier nations, characterized by fragmented surveillance networks, chronic underfunding of emergency protocols, and a lack of robust biosecurity measures. The hybrid framework introduced here significantly advances methodological rigor in health security research, offering a robust and replicable model for regional benchmarking and comparative analysis. Cross-regional comparisons illuminated context-specific priorities, with the EU’s emphasis on detection and rapid response contrasting sharply with Africa’s prevention-centric strategies, highlighting the need for tailored interventions. These findings underscore the imperative for collaborative, tiered interventions—leveraging high-performing clusters as best-practice models while strategically channelling resources and technical assistance to vulnerable states to address their unique deficits. For EU policymakers, this study provides a roadmap for actionable strategies: strengthening and harmonizing data-sharing protocols under the European Health Union (EHU), prioritizing targeted infrastructure investments in critically vulnerable member states like Cyprus, Luxembourg, Malta, and Romania, and institutionalizing dynamic, continuous monitoring to track progress and adapt to evolving threats. However, this study is not without its limitations. The reliance on available data may introduce biases, and the entropy method, while robust, may not capture all nuances of health security dynamics. Future research should explore longitudinal studies that incorporate qualitative assessments to enrich quantitative findings. Additionally, expanding the analysis to include other regions could enhance the understanding of global health security patterns. By effectively bridging methodological and operational gaps, this work contributes to a more resilient, equitable, and proactive health security landscape, not only within the EU but also as a model for other regions. Future efforts must build upon these insights, ensuring that preparedness strategies evolve in tandem with emerging global health threats and are underpinned by robust, data-driven frameworks. Ethics and consent No Ethical approval or consent needed. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work the author(s) used [DeepSeek v3, Paperpal, Quillbot, and ChatGPT] for language refinement and structure. After using this tools, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication. Data availability statement Underlying data The data supporting the findings of this study are publicly available and can be accessed through the following repository (Global Health Security Index, Global Health Security Index Data Model and Report (2021), at https://ghsindex.org/report-model/ ). 37 Extended data Figshare: Unveiling Health Security Patterns in the European Union_Supplementary Document. Doi: https://doi.org/10.6084/m9.figshare.28898594.v1 . 42 The project contains the following underlying data: Unveiling Health Security Patterns in the European Union_Supplementary Document.xlsx. All data, and processing results related to this study are presented in this file. This file integrates the processes of weighting, ranking, and clustering analyses into a single Excel-based tool, offering a comprehensive framework for analysis the Health Security in EU Countries. This source also includes the values behind the results reported. This file also includes the values behind the measures reported in all analysis and discussion sections, as well as the values used to construct tables and figures. This supplementary resource also provides detailed support for replicating the study’s methods and results. This data are publicly available and can be accessed through the following repository ( https://figshare.com/articles/dataset/Unveiling_Health_Security_Patterns_in_the_European_Union_Supplementary_Document/28898594 ) and archived via [ https://doi.org/10.6084/m9.figshare.28898594.v1 ]. 42 Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Acknowledgments Not applicable. References 1. Rose SM, Paterra M, Isaac C, et al. : Analysing COVID-19 outcomes in the context of the 2019 Global Health Security (GHS) Index. BMJ Glob. Health. 2021; 6 (12): e007581. PubMed Abstract | Publisher Full Text | Free Full Text 2. Neogi S, Preetha G: Assessing health systems’ responsiveness in tackling COVID-19 pandemic. Indian J. Public Health. 2020; 64 (6): 211–216. Publisher Full Text 3. Tappero JW, et al. : US Centers for Disease Control and Prevention and Its Partners’ Contributions to Global Health Security. Emerg. Infect. Dis. 2017; 23 (13): S5–S14. PubMed Abstract | Publisher Full Text | Free Full Text 4. Buliva E, Elhakim M, et al. : Emerging and Reemerging Diseases in the World Health Organization (WHO) Eastern Mediterranean Region—Progress, Challenges, and WHO Initiatives. Front. Public Health. 2017; 5 : 276. PubMed Abstract | Publisher Full Text | Free Full Text 5. Nasser AA, Alghawli ASA: Evaluation and clustering of health security performance in Africa: A comparative analysis through the entropy-TOPSIS-K-means approach. Afr. Secur. Rev. 2024; 33 (3): 330–348. Publisher Full Text 6. Nasser AA, Alghawli ASA, et al. : Health Security Disparities in the Eastern Mediterranean Region: A Comparative Analysis Using an Integrated MCDM and Clustering Approach. J. Biosaf. Biosecur. 2025; 7 (1): 38–51. Publisher Full Text 7. Standley CJ, Fogarty AS, et al. : One Health Systems Assessments for Sustainable Capacity Strengthening to Control Priority Zoonotic Diseases Within and Between Countries. Risk Manag. Healthc. Policy. 2023; 16 : 2497–2504. PubMed Abstract | Publisher Full Text | Free Full Text 8. Gökalp Y, Dinçer H, et al. : Generating a Novel Artificial Intelligence-Based Decision-Making Model for Determining Priority Strategies for Improving Community Health. J. Oper. Intell. 2024; 2 (1): 1–13. Publisher Full Text 9. Bell JA, Nuzzo JB: Advancing Collective Action and accountability Amid Global Crisis. Global Health Security Index. December 2021. Accessed October 21, 2024. Reference Source 10. Nasser AA, Alghawli ASA, et al. : Income-Based analysis of health security in Western Asia through an integrated GHSI, MCDM, and Clustering Model. F1000Res. 2025; 14 (43): 43. PubMed Abstract | Publisher Full Text | Free Full Text 11. Forman R, Mossialos E: The EU Response to COVID-19: From Reactive Policies to Strategic Decision-Making. JCMS-J COMMON MARK S. 2021; 59 (S1): 56–68. PubMed Abstract | Publisher Full Text | Free Full Text 12. Rees GH, Batenburg R, Scotter C: Responding to COVID-19: an exploration of EU country responses and directions for further research. BMC Health Serv. Res. 2024; 24 (1): 1198. PubMed Abstract | Publisher Full Text | Free Full Text 13. Lupu D, Tiganasu R: COVID-19 and the efficiency of health systems in Europe. Health. Econ. Rev. 2022; 12 (1): 14. PubMed Abstract | Publisher Full Text | Free Full Text 14. Spieske A, Gebhardt M, et al. : Improving resilience of the healthcare supply chain in a pandemic: Evidence from Europe during the COVID-19 crisis. J. Purch. Supply Manag. 2022; 28 (5): 100748. Publisher Full Text 15. León-Figueroa DA, Bonilla-Aldana DK, et al. : The never-ending global emergence of viral zoonoses after COVID-19? The rising concern of monkeypox in Europe, North America and beyond. Travel Med. Infect. Dis. 2022; 49 : 102362. PubMed Abstract | Publisher Full Text | Free Full Text 16. Brown GW, Bridge G, et al. : The role of health systems for health security: a scoping review revealing the need for improved conceptual and practical linkages. Glob. Health. 2022; 18 (1): 51. PubMed Abstract | Publisher Full Text | Free Full Text 17. El Samad M, El Nemar S, et al. : An innovative big data framework for exploring the impact on decision-making in the European Mediterranean healthcare sector. EuroMed J. Bus. 2022; 17 (3): 312–332. Publisher Full Text 18. Said MM, Nasser AA, Alkhulaidi AA: Prioritization of the eco-hotels performance criteria in Yemen using fuzzy Delphi method. Int. J. Appl. Inf. Syst. 2021; 12 (36): 20–29. Publisher Full Text 19. Nasser AA, Saeed MM, Aljober MN: Application of selected MCDM methods for developing a multi-functional framework for eco-hotel planning in Yemen. Int. J. Comput. Sci. Eng. 2021; 9 (10): 7–18. Publisher Full Text 20. Alghawli ASA, Nasser AA, Aljober MN: A fuzzy MCDM approach for structured comparison of the health literacy level of hospitals. Int. J. Adv. Comput. Sci. Appl. 2021; 12 (7). Publisher Full Text 21. Al-Khulaidi AAG, Nasser AA, Al-Ashwal MHY, et al. : Investigating information security risk management in Yemeni banks: An CILOS-TOPSIS approach. Multidisc. Sci. J. 2024; 6 (9): 2024175. Publisher Full Text 22. Nasser AA, et al. : Assessing equity in healthcare facility resource allocation in Yemen: An entropy-TOPSIS analysis. Int. J. Recent Innov. Trends Comput. Commun. 2023; 11 (9): 1598–1609. Publisher Full Text 23. Pérez-Gladish B, Ferreira FAF, Zopounidis C: MCDM/A studies for economic development, social cohesion and environmental sustainability: introduction. Int. J. Sustain. Dev. World Ecol. 2020 Sep 13; 28 (1): 1–3. Publisher Full Text 24. Pereira J, Contreras P, Morais DC, et al. : A multi-criteria and stochastic robustness analysis approach to compare nations' sustainability. Socio Econ. Plan. Sci. 2022; 80 : 101159. Publisher Full Text 25. Yadav R, Singh M, Lee SY, et al. : Selection and ranking of dental restorative composite materials using hybrid entropy-VIKOR method: An application of MCDM technique. Publisher Full Text 26. Mallick R, Pramanik S, Giri BC: TOPSIS and VIKOR strategies for COVID-19 vaccine selection in QNN environment. OPSEARCH. 2024; 61 (4): 2072–2094. Publisher Full Text 27. Gupta S, Vijayvargy L, Gupta K: Assessment of stress level in urban areas during COVID-19 outbreak using CRITIC and TOPSIS: A case of Indian cities. J. Stat. Manag. Syst. 2021; 24 (2): 411–433. Publisher Full Text 28. Ikotun AM, Almutari MS, Ezugwu AE: K-means-based nature-inspired metaheuristic algorithms for automatic data clustering problems: Recent advances and future directions. Appl. Sci. 2021; 11 (23). Publisher Full Text 29. Miraftabzadeh SM, et al. : K-means and alternative clustering methods in modern power systems. IEEE Access. 2023; 11 : 119596–119633. Publisher Full Text 30. Nasser AA, Alkhulaidi AA, Ali MN, et al. : A weighted Euclidean distance - statistical variance procedure based approach for improving the healthcare decision making system in Yemen. Indian J. Sci. Technol. 2019; 12 (3): 1–15. Publisher Full Text 31. Nasser AA, Alkhulaidi AA, Ali MN, et al. : A study on the impact of multiple methods of data normalization on the result of SAW, WED, and TOPSIS ordering in healthcare multi-attributes decision making systems based on EW, ENTROPY, CRITIC, and SVP weighting approaches. Indian J. Sci. Technol. 2019; 12 (4): 1–21. Publisher Full Text 32. Alghawli AS, Al-khulaidi AA, Nasser AA, et al. : Application of the Fuzzy Delphi Method to identify and prioritize the social-health family disintegration indicators in Yemen. Int. J. Adv. Comput. Sci. Appl. 2022; 13 (5). Publisher Full Text 33. Zhu Y, Zeng S, et al. : Comprehensive evaluation and spatial-temporal differences analysis of China’s inter-provincial doing business environment based on Entropy-CoCoSo method. Front. Environ. Sci. 2023; 10 . Publisher Full Text 34. Aljober MN, Nasser AA, Alghawli ASA, et al. : Health Security inequalities in Non-EU European Countries: A Cross-National Comparative Assessment Using an Integrated MCDM-Machine Learning Approach. F1000Res. 2025; 14 (462). Publisher Full Text 35. Shaaban S, El-latif A: Integration of evaluation distance from average solution approach with information entropy weight for diesel engine parameter optimization. Int. J. Intell. Eng. Syst. 2020; 13 (3): 101–111. Publisher Full Text 36. Dwivedi PP, Sharma DK: Application of Shannon entropy and CoCoSo methods in selection of the most appropriate engineering sustainability components. Clean. Mater. 2022; 5 : 100118. Publisher Full Text 37. Global Health Security Index-Data model. 2021. Accessed April 30, 2024. Reference Source 38. Assefa Y, Hill PS, Gilks CF, et al. : Global health security and universal health coverage: Understanding convergences and divergences for a synergistic response. Agyepong I, editor. PLOS ONE. 2020 Dec 30; 15 (12): e0244555. PubMed Abstract | Publisher Full Text | Free Full Text 39. Wang Z, Duan Y, Jin Y, et al. : Coronavirus disease 2019 (COVID-19) pandemic: how countries should build more resilient health systems for preparedness and response. Global Health J. 2020 Dec; 4 (4): 139–145. PubMed Abstract | Publisher Full Text | Free Full Text 40. Shannon CE: A mathematical theory of communication. Bell Syst. Tech. J. 1948; 27 (3): 379–423. Publisher Full Text 41. Kumar R, Singh S, et al. : Revealing the benefits of entropy weights method for multi-objective optimization in machining operations: A critical review. J. Mater. Res. Technol. 2021; 10 : 1471–1492. Publisher Full Text 42. Nasser AA, Ali Al-Samawi Y, Alghawli ASA, et al. : Unveiling Health Security Patterns in the European Union_Supplementary Document. Dataset. figshare. 2025. Publisher Full Text 43. Yazdani M, Zarate P, et al. : A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Manag. Decis. 2019; 57 (9): 2501–2519. Publisher Full Text 44. Qi J, Yu Y, Wang L, et al. : K*-Means: An effective and efficient K-Means clustering algorithm. 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom). 2016; pp. 242–249. Publisher Full Text 45. Shameem M-U-S, Ferdous R: An efficient k-means algorithm integrated with Jaccard distance measure for document clustering. 2009 First Asian Himalayas International Conference on Internet. 2009; pp. 1–6. Publisher Full Text 46. Velina L: Health-related measures in the national recovery and resilience plans, EPRS: European Parliamentary Research Service. Belgium: 2023. on 28 Sep 2024. COI: 20.500.12592/8jtrdz. Reference Source 47. Montás MC, Klasa K, van Ginneken E , et al. : Strategic purchasing and health systems resilience: Lessons from COVID-19 in selected European countries. Health Policy. 2022 Sep; 126 (9): 853–864. PubMed Abstract | Publisher Full Text | Free Full Text 48. Ibrahim M, Abdelmagid N, et al. : Finding the fragments: community-based epidemic surveillance in Sudan. Glob. Health Res. Polic. 2023; 8 (1): 20. PubMed Abstract | Publisher Full Text | Free Full Text 49. Chingonzoh R, Gixela Y, et al. : Public health surveillance perspectives from provincial COVID-19 experiences, South Africa 2021. Jàmbá J. Disaster Risk Stud. 2024; 16 (1). PubMed Abstract | Publisher Full Text | Free Full Text 50. Muhammed A, Flaih L: Predictive modeling in healthcare: A survey of data mining applications. Proceedings of the 5th International Conference on Communication Engineering and Computer Science (CIC-COCOS’24). 2024; 1–11. Publisher Full Text 51. Alhegami AS, Alsaeedi HA: A framework for incremental parallel mining of interesting association patterns for big data. Int. J. Comput. Dent. 2020 Mar 31; 106–117. Publisher Full Text 52. Flaih LR, Baha al-Deen SA, Ayoobkhan MUA: Analysis of surface quality measurement with classification approach. J. Phys. Conf. Ser. 2020 Dec 1; 1712 (1): 012027. Publisher Full Text 53. Al-Hegami AS, Al-Hddad NSA, Alsaeedi HA: A web mining approach for evaluation of quality assurance at University of Science and Technology. J. Eng. Technol. Sci. - JOEATS. 2025; 3 (1): 146–158. Publisher Full Text 54. Marcus JS, Martens B, Carugati C, et al. : The European Health Data Space. SSRN Electron. J. 2022. Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 19 Jun 2025 ADD YOUR COMMENT Comment Author details Author details 1 Department of Information Systems and Computer Science, Sa’adah University, Sa’adah University, Sa’adah, Yemen 2 Department of Artificial Intelligence, Modern Specialized University, Sana'a, Yemen 3 Information Technology, Modern Specialized University, Sana'a, Yemen 4 Department of Computer Science, College of Sciences and Humanities, Prince Sattam bin Abdulaziz University, Al Kharj, Riyadh Province, 16700, Saudi Arabia Adel A. Nasser Roles: Conceptualization, Formal Analysis, Methodology, Software, Supervision, Writing – Original Draft Preparation Yahya Ali Al-Samawi Roles: Conceptualization, Investigation, Resources, Validation, Writing – Review & Editing Abed Saif Ahmed Alghawli Roles: Data Curation, Formal Analysis, Funding Acquisition, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Amani A. K. Essayed Roles: Software, Visualization, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This study was supported via funding from Deanship of Scientific Research, Prince Sattam bin Abdulaziz University [project number: PSAU/2025/R/1446]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 19 Jun 2025, 14:600 https://doi.org/10.12688/f1000research.166187.1 Copyright © 2025 Nasser AA et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Nasser AA, Al-Samawi YA, Alghawli ASA and Essayed AAK. Unveiling Health Security Patterns in the European Union through a Hybrid Entropy-CoCoSo and K-Means Clustering Framework [version 1; peer review: 2 approved] . F1000Research 2025, 14 :600 ( https://doi.org/10.12688/f1000research.166187.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 19 Jun 2025 Views 0 Cite How to cite this report: Al-Shqeerat APKH. Reviewer Report For: Unveiling Health Security Patterns in the European Union through a Hybrid Entropy-CoCoSo and K-Means Clustering Framework [version 1; peer review: 2 approved] . F1000Research 2025, 14 :600 ( https://doi.org/10.5256/f1000research.183140.r393598 ) The direct URL for this report is: https://f1000research.com/articles/14-600/v1#referee-response-393598 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 13 Aug 2025 Assoc. Prof. Khalil Hamdi Al-Shqeerat , Qassim University, Buraydah, Al Qassim, Saudi Arabia Approved VIEWS 0 https://doi.org/10.5256/f1000research.183140.r393598 Manuscript "Unveiling Health Security Patterns in the European Union through a Hybrid Entropy-CoCoSo and K-Means Clustering Framework", Naser A. A.,et al. This manuscript is a methodologically robust and timely addition to the health security research. It offers a well-designed ... Continue reading READ ALL Manuscript "Unveiling Health Security Patterns in the European Union through a Hybrid Entropy-CoCoSo and K-Means Clustering Framework", Naser A. A.,et al. This manuscript is a methodologically robust and timely addition to the health security research. It offers a well-designed framework for assessing pandemic preparedness among EU member states from a data-driven, context-sensitive perspective. The integration of Entropy-CoCoSo and K-means provides a strategically balanced approach that combines analytical rigor with practical relevance. The inclusion of cross-regional comparisons with Africa and the EMR enhances the global applicability of this study. With clear exposition, comprehensive data, and practical insights, this study is highly pertinent to both scholars and policymakers. Its capacity to translate complex methods into replicable, actionable guidance solidifies its value as a reference for health ministries and regional preparedness agencies. Although the manuscript is already well developed, a few minor adjustments could further enhance its clarity and overall polish. Introduction: When discussing the EHU and HERA, we briefly compare their structures with the WHO frameworks to underscore the distinctive governance complexity in the EU. Additionally, please expand the final paragraph of the Introduction to clearly define the manuscript's core contributions and how they directly address the stated research questions. Methodology: Provide concise justifications for choosing CoCoSo over other MCDM techniques, emphasizing its adaptability, interpretability, and empirical success in similar domains. Results: Following Table 10, a summary paragraph that highlights the countries experiencing the most significant rank shifts, both upward and downward, and comments on any unexpected changes in tier positions. Discussion: This section emphasizes a few surprising findings, such as Bulgaria’s strong performance, and contextualizes them in relation to known health system characteristics or reforms. Conclusion: Conclude with a forward-looking paragraph that briefly reflects on the limitations (even if reiterated in Section 4.6), outlines directions for future research, and advocates regional adoption or mentorship programs to foster solidarity across clusters. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Security, Wireless Sensors Networks, Cloud Computing, Artificial Intelligence. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Al-Shqeerat APKH. Reviewer Report For: Unveiling Health Security Patterns in the European Union through a Hybrid Entropy-CoCoSo and K-Means Clustering Framework [version 1; peer review: 2 approved] . F1000Research 2025, 14 :600 ( https://doi.org/10.5256/f1000research.183140.r393598 ) The direct URL for this report is: https://f1000research.com/articles/14-600/v1#referee-response-393598 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Abdu APNAA. Reviewer Report For: Unveiling Health Security Patterns in the European Union through a Hybrid Entropy-CoCoSo and K-Means Clustering Framework [version 1; peer review: 2 approved] . F1000Research 2025, 14 :600 ( https://doi.org/10.5256/f1000research.183140.r393596 ) The direct URL for this report is: https://f1000research.com/articles/14-600/v1#referee-response-393596 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 29 Jul 2025 Assis. Prof.Nail Adeeb Ali Abdu , University of Science and Technology, Yeman, Saudi Arabia; University of Science and Technology, Aden, Yemen Approved VIEWS 0 https://doi.org/10.5256/f1000research.183140.r393596 This manuscript makes a significant and timely contribution to the field of health security analytics. By integrating Entropy-CoCoSo and K-means clustering, it introduces a novel methodological approach that is both rigorous and well-executed. The paper demonstrates exceptional analytical depth, particularly ... Continue reading READ ALL This manuscript makes a significant and timely contribution to the field of health security analytics. By integrating Entropy-CoCoSo and K-means clustering, it introduces a novel methodological approach that is both rigorous and well-executed. The paper demonstrates exceptional analytical depth, particularly in its cross-regional comparison of health security priorities across the EU, Africa, and the Eastern Mediterranean Region (EMR). The clarity in methodological articulation and the robust data visualization throughout the study enhance its impact, making the findings highly accessible to both policymakers and academic audiences. Furthermore, the authors are commended for their comprehensive documentation of each computational stage, the thoughtful discussion of entropy dynamics across regions, and the practical implications of the clustering results. The policy relevance—especially concerning European Health Union priorities—is clearly articulated, and the dataset employed is transparent and replicable. Nonetheless, a few revisions could further enhance the manuscript: 1. Abstract: Consider quantifying key results (e.g., entropy weights or number of countries per cluster) to underscore the analytical depth of your framework. 2. Introduction: The comparative regional framing (EU vs. Africa and EMR) is excellent. You might elevate this with a sentence summarizing why these two regions were selected for benchmark comparison. 3. Methods: Clarify the rationale for comparing 2019, 2021, and aggregated (2017–2021) datasets. A sentence explaining the added value of this temporal analysis would solidify methodological transparency. 4. Discussion: When discussing shifts in entropy weights, it could be beneficial to briefly connect these to real-world policy developments in Europe (e.g., HERA’s activation post-2021). 5. Conclusion: Consider explicitly stating how your approach could be adapted by health policymakers or by global health institutions such as WHO or ECDC. I recommend accepting the paper with minor revisions to address the comments outlined above (Approved). Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Machine learning, health information systems, Information security, Blockchain. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Abdu APNAA. Reviewer Report For: Unveiling Health Security Patterns in the European Union through a Hybrid Entropy-CoCoSo and K-Means Clustering Framework [version 1; peer review: 2 approved] . F1000Research 2025, 14 :600 ( https://doi.org/10.5256/f1000research.183140.r393596 ) The direct URL for this report is: https://f1000research.com/articles/14-600/v1#referee-response-393596 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 19 Jun 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 1 19 Jun 25 read read Assis. Prof.Nail Adeeb Ali Abdu , University of Science and Technology, Yeman, Saudi Arabia; University of Science and Technology, Aden, Yemen Assoc. Prof. Khalil Hamdi Al-Shqeerat , Qassim University, Buraydah, Saudi Arabia Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Al-Shqeerat A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 13 Aug 2025 | for Version 1 Assoc. Prof. Khalil Hamdi Al-Shqeerat , Qassim University, Buraydah, Al Qassim, Saudi Arabia 0 Views copyright © 2025 Al-Shqeerat A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Manuscript "Unveiling Health Security Patterns in the European Union through a Hybrid Entropy-CoCoSo and K-Means Clustering Framework", Naser A. A.,et al. This manuscript is a methodologically robust and timely addition to the health security research. It offers a well-designed framework for assessing pandemic preparedness among EU member states from a data-driven, context-sensitive perspective. The integration of Entropy-CoCoSo and K-means provides a strategically balanced approach that combines analytical rigor with practical relevance. The inclusion of cross-regional comparisons with Africa and the EMR enhances the global applicability of this study. With clear exposition, comprehensive data, and practical insights, this study is highly pertinent to both scholars and policymakers. Its capacity to translate complex methods into replicable, actionable guidance solidifies its value as a reference for health ministries and regional preparedness agencies. Although the manuscript is already well developed, a few minor adjustments could further enhance its clarity and overall polish. Introduction: When discussing the EHU and HERA, we briefly compare their structures with the WHO frameworks to underscore the distinctive governance complexity in the EU. Additionally, please expand the final paragraph of the Introduction to clearly define the manuscript's core contributions and how they directly address the stated research questions. Methodology: Provide concise justifications for choosing CoCoSo over other MCDM techniques, emphasizing its adaptability, interpretability, and empirical success in similar domains. Results: Following Table 10, a summary paragraph that highlights the countries experiencing the most significant rank shifts, both upward and downward, and comments on any unexpected changes in tier positions. Discussion: This section emphasizes a few surprising findings, such as Bulgaria’s strong performance, and contextualizes them in relation to known health system characteristics or reforms. Conclusion: Conclude with a forward-looking paragraph that briefly reflects on the limitations (even if reiterated in Section 4.6), outlines directions for future research, and advocates regional adoption or mentorship programs to foster solidarity across clusters. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Security, Wireless Sensors Networks, Cloud Computing, Artificial Intelligence. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Al-Shqeerat APKH. Peer Review Report For: Unveiling Health Security Patterns in the European Union through a Hybrid Entropy-CoCoSo and K-Means Clustering Framework [version 1; peer review: 2 approved] . F1000Research 2025, 14 :600 ( https://doi.org/10.5256/f1000research.183140.r393598) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-600/v1#referee-response-393598 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Abdu A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 29 Jul 2025 | for Version 1 Assis. Prof.Nail Adeeb Ali Abdu , University of Science and Technology, Yeman, Saudi Arabia; University of Science and Technology, Aden, Yemen 0 Views copyright © 2025 Abdu A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This manuscript makes a significant and timely contribution to the field of health security analytics. By integrating Entropy-CoCoSo and K-means clustering, it introduces a novel methodological approach that is both rigorous and well-executed. The paper demonstrates exceptional analytical depth, particularly in its cross-regional comparison of health security priorities across the EU, Africa, and the Eastern Mediterranean Region (EMR). The clarity in methodological articulation and the robust data visualization throughout the study enhance its impact, making the findings highly accessible to both policymakers and academic audiences. Furthermore, the authors are commended for their comprehensive documentation of each computational stage, the thoughtful discussion of entropy dynamics across regions, and the practical implications of the clustering results. The policy relevance—especially concerning European Health Union priorities—is clearly articulated, and the dataset employed is transparent and replicable. Nonetheless, a few revisions could further enhance the manuscript: 1. Abstract: Consider quantifying key results (e.g., entropy weights or number of countries per cluster) to underscore the analytical depth of your framework. 2. Introduction: The comparative regional framing (EU vs. Africa and EMR) is excellent. You might elevate this with a sentence summarizing why these two regions were selected for benchmark comparison. 3. Methods: Clarify the rationale for comparing 2019, 2021, and aggregated (2017–2021) datasets. A sentence explaining the added value of this temporal analysis would solidify methodological transparency. 4. Discussion: When discussing shifts in entropy weights, it could be beneficial to briefly connect these to real-world policy developments in Europe (e.g., HERA’s activation post-2021). 5. Conclusion: Consider explicitly stating how your approach could be adapted by health policymakers or by global health institutions such as WHO or ECDC. I recommend accepting the paper with minor revisions to address the comments outlined above (Approved). Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Machine learning, health information systems, Information security, Blockchain. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Abdu APNAA. Peer Review Report For: Unveiling Health Security Patterns in the European Union through a Hybrid Entropy-CoCoSo and K-Means Clustering Framework [version 1; peer review: 2 approved] . F1000Research 2025, 14 :600 ( https://doi.org/10.5256/f1000research.183140.r393596) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-600/v1#referee-response-393596 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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