Risk assessment approach for analyzing risk factors to overcome pandemic using interval-valued q-rung orthopair fuzzy decision making method


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ŞEKER Ş., Bağlan F. B., AYDIN N., Deveci M., Ding W.

Applied Soft Computing, cilt.132, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 132
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.asoc.2022.109891
  • Dergi Adı: Applied Soft Computing
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC
  • Anahtar Kelimeler: COPRAS, Fuzzy sets, Interval valued, q-ROFSs, Risk assessment
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

The process of developing and implementing sustainable strategies to prevent spread of COVID-19 for society typically requires integrating all social, technological, economic, governmental aspects in a systematic way. Since the clear understanding of risk factors contribute to the success of the strategies applied against COVID-19, a risk assessment procedure is applied in this study to properly evaluate risk factors cause to spread of pandemic as a multi-complex decision problem. Therefore, due to the evaluation of risk factors, which often involves uncertain information, the model is constructed based on interval-valued q-rung orthopair fuzzy-COmplex PRoportional ASsessment (IVq-ROF-COPRAS) method. While the developed framework is efficient to enhance the quality of decisions by implementing more realistic, precise, and effective application procedure under uncertain environment, it has capability to help governments for developing comprehensive strategies and responses. According to the results of the proposed risk analysis model, the top three risk factors are “The Approach that Prioritizes the Economy in Policies”, “Insufficient Process Control in Normalization” and “Lack of Epidemic Management Culture in Individuals and Businesses”. Lastly, to show applicability and efficiency of the model sensitivity and comparative analysis were conducted at the end of the study.