Evaluating the performance of countries in COVID-19 management: A data-driven decision-making and clustering

Basit Öğe Kaydı
dc.authorscopusidArdavan Babaei / 57193898673
dc.authorscopusidErfan Babaee Tirkolaee / 57196032874
dc.authorwosidArdavan Babaei / JLG-3040-2023
dc.authorwosidErfan Babaee Tirkolaee / U-3676-2017
dc.contributor.authorMeraji, Hamed
dc.contributor.authorRahimi, Danial
dc.contributor.authorBabaei, Ardavan
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2025-04-17T13:27:40Z
dc.date.available2025-04-17T13:27:40Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü
dc.description.abstractThe COVID-19 outbreak, first reported in Wuhan, China, spread rapidly and endangered human lives and livelihoods globally. Researchers have utilized available tools and facilities to mitigate its impact across dimensions. In this study, we propose a comprehensive, data-driven framework to evaluate periodically 168 countries' performance, considering four distinct variable categories since the advent of COVID-19. We assess and leverage four clustering methods of K-means, Gaussian Mixture Model (GMM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Spectral, as well as three Multi-Criteria Decision-Making (MCDM) approaches, including Combined Compromise Solution (COCOSO), Grey Relational Analysis (GRA), and Evaluation Based on Distance from Average Solution (EDAS) for ranking the countries. The results are analyzed thoroughly-among the examined factors, "Total Recovered", "GDP Per capita", and "Hospital Beds / 1 K" most critically impacted evaluating outcomes, while" Male Smokers", "Diabetes Prevalence", and "Cardiovascular Death Rate" are least influential. The novel metric "Medical Waste" also demonstrates more vital than 86 % of existing indicators. Moreover, the findings reveal associations between countries' development levels and their corresponding cluster assignments. For more precise analysis, we investigate the intra-cluster and inter-cluster approaches, each of which revealed countries' promotion or degradation regarding rankings within a cluster or transitions between clusters. Finally, appropriate policy-making and management strategies are presented to enhance countries' preparedness for potential future outbreaks based on the results.
dc.identifier.citationMeraji, H., Rahimi, D., Babaei, A., & Tirkolaee, E. B. (2025). Evaluating the performance of countries in COVID-19 management: A data-driven decision-making and clustering. Applied Soft Computing, 169, 112549.
dc.identifier.doi10.1016/j.asoc.2024.112549
dc.identifier.endpage21
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85211063062
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1016/j.asoc.2024.112549
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6269
dc.identifier.volume169
dc.identifier.wosWOS:001374485200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBabaei, Ardavan
dc.institutionauthorTirkolaee, Erfan Babaee
dc.institutionauthoridArdavan Babaei / 0000-0002-3657-4853
dc.institutionauthoridErfan Babaee Tirkolaee / 0000-0003-1664-9210
dc.language.isoen
dc.publisherElsevier ltd
dc.relation.ispartofApplied soft computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectClustering
dc.subjectCOVID-19 Management
dc.subjectData-Driven Decision-Making
dc.subjectMCDM
dc.subjectPerformance Evaluation
dc.titleEvaluating the performance of countries in COVID-19 management: A data-driven decision-making and clustering
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
Ä°sim:
1-s2.0-S1568494624013231-main.pdf
Boyut:
2.99 MB
Biçim:
Adobe Portable Document Format
Ä°ndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
Ä°sim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
Ä°ndir

Koleksiyon

Endüstri Mühendisliği Bölümü Makale Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu