Volume 11, Issue 2 (2025)
IEM 2025, 11(2): 0-0 |
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Ahmadi O. Identification and prioritization of safety barriers to prevent and reduce the infection of the COVID-19 using fuzzy DEMATEL-BAYESIAN modeling: lesson learned. IEM 2025; 11 (2)
URL: http://iem.modares.ac.ir/article-4-76404-en.html
URL: http://iem.modares.ac.ir/article-4-76404-en.html
Department of Occupational Health Engineering, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran , o.ahmadi@modares.ac.ir
Abstract: (87 Views)
Background: The COVID-19 pandemic resulted in widespread outbreaks and a significant increase in mortality among both the general population and the workforce over a span of two years. This study aimed to identify and prioritize measures for preventing and reducing the incidence of COVID-19 through the application of fuzzy DEMATEL-Bayesian modeling.
Materials & Methods: In the first phase, key factors in the prevention and reduction of COVID-19, as identified in past studies, were reviewed and extracted. In the second phase, the cause-and-effect relationships of these factors in the prevention and control of COVID-19 were established using the fuzzy DEMATEL method. In the third phase, the identified factors were integrated into a Bayesian network based on the findings from the previous phase.
Findings: The analysis identified seven critical factors in the prevention and control of COVID-19: personal protective equipment, social distancing, technology, training, lessons learned, geographical factors, and attention to sensitive age groups. The results indicated that the prevention and reduction node of COVID-19 was most sensitive to social distancing, more so than any other factor.
Conclusion: Based on the sensitivity analysis of the model, the first priority in decision-making for preventing and reducing COVID-19 should be focused on social distancing. The Bayesian network model developed in this study can effectively assist in macro-level decision-making by prioritizing the measures necessary to control and reduce the spread of the COVID-19.
Materials & Methods: In the first phase, key factors in the prevention and reduction of COVID-19, as identified in past studies, were reviewed and extracted. In the second phase, the cause-and-effect relationships of these factors in the prevention and control of COVID-19 were established using the fuzzy DEMATEL method. In the third phase, the identified factors were integrated into a Bayesian network based on the findings from the previous phase.
Findings: The analysis identified seven critical factors in the prevention and control of COVID-19: personal protective equipment, social distancing, technology, training, lessons learned, geographical factors, and attention to sensitive age groups. The results indicated that the prevention and reduction node of COVID-19 was most sensitive to social distancing, more so than any other factor.
Conclusion: Based on the sensitivity analysis of the model, the first priority in decision-making for preventing and reducing COVID-19 should be focused on social distancing. The Bayesian network model developed in this study can effectively assist in macro-level decision-making by prioritizing the measures necessary to control and reduce the spread of the COVID-19.
Article Type: Original Research |
Subject:
Virology
Received: 2024/08/4 | Accepted: 2025/01/20 | Published: 2025/04/21
Received: 2024/08/4 | Accepted: 2025/01/20 | Published: 2025/04/21
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