Country-level assessment of COVID-19 performance: A cluster-based MACONT-CRITIC analysis

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dc.authorscopusidArdavan Babaei / 57193898673
dc.authorwosidArdavan Babaei / JLG-3040-2023
dc.contributor.authorAmiri, Amirreza Salehi
dc.contributor.authorBabaei, Ardavan
dc.contributor.authorKhedmati, Majid
dc.date.accessioned2025-04-17T12:54:12Z
dc.date.available2025-04-17T12:54:12Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü
dc.description.abstractCOVID-19, a highly contagious respiratory virus, emerged in Wuhan in December 2019, leading to a global health emergency and subsequent pandemic declaration. Despite preventive measures, millions have been diagnosed and millions more have lost their lives, highlighting the urgent need for efficient diagnostics and effective interventions. This study presents a comprehensive framework based on integrated machine learningdecision making (ML-MCDM) to assess and compare the performance of countries during the COVID-19. The aim is to evaluate the performance of countries and identify the effective strategies for controlling the pandemic. The framework introduces a new criterion entitled 'Resilience' which aims to assess a country's capability to address peak diseases by identifying the occurrence of peaks and calculating the duration between the peak and the return to a normal state. Then, it employs K-Means clustering to group countries based on their performance indicators. The countries are then ranked within each cluster using the CRITIC-MACONT framework. The present study introduces a novel approach by integrating MACONT and CRITIC methodologies, marking the first instance of such integration. Additionally, the incorporation of machine learning techniques enhances their proficiency in effectively ranking the alternatives. The results of the analysis, conducted until March 2023, using the COVID-19 dataset, demonstrate that four clusters effectively evaluate the performance of countries and, the 'Resilience' criterion emerges as the most significant among the evaluated criteria. Based on the results, the proposed framework effectively ranks the countries and provides valuable insights for pandemic control strategies.
dc.identifier.citationAmiri, A. S., Babaei, A., & Khedmati, M. (2025). Country-Level Assessment of COVID-19 Performance: A Cluster-Based MACONT-CRITIC Analysis. Applied Soft Computing, 112762.
dc.identifier.doi10.1016/j.asoc.2025.112762
dc.identifier.endpage12
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85216585167
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1016/j.asoc.2025.112762
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6260
dc.identifier.volume171
dc.identifier.wosWOS:001420332100001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBabaei, Ardavan
dc.institutionauthoridArdavan Babaei / 0000-0002-3657-4853
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 Pandemic
dc.subjectMachine Learning
dc.subjectMulti-Criteria Decision Making
dc.subjectResilience
dc.titleCountry-level assessment of COVID-19 performance: A cluster-based MACONT-CRITIC analysis
dc.typeArticle

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