Country-level assessment of COVID-19 performance: A cluster-based MACONT-CRITIC analysis
Yükleniyor...
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
COVID-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.
Açıklama
Anahtar Kelimeler
Clustering, COVID-19 Pandemic, Machine Learning, Multi-Criteria Decision Making, Resilience
Kaynak
Applied soft computing
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
171
Sayı
Künye
Amiri, A. S., Babaei, A., & Khedmati, M. (2025). Country-Level Assessment of COVID-19 Performance: A Cluster-Based MACONT-CRITIC Analysis. Applied Soft Computing, 112762.
