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

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.