Identification and prioritization of safety barriers to prevent and reduce the infection of the COVID-19 using fuzzy DEMATEL-BAYESIAN modeling: lesson learned

Document Type : Original Research

Author
O. Ahmadi
Department of Occupational Health Engineering, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
10.61186/iem.11.2.135
Abstract
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.

Keywords

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Infection Epidemiology and Microbiology
Volume 11, Issue 2
April 2025
Pages 135-165