Fuzzy Logic-based Risk Analysis of COVID-19 Infection: A Case Study in Healthcare Facilities

Background: The gravity point of all management systems in the new approach of global worldwide standards includes management and assessment of risks and opportunities. Although the spread of COVID-19 as a global pandemic has threatened the health of the workforce and caused catastrophic human and economic consequences, the occurrence of this global challenge has also created opportunities to pay more attention to the risk assessment of biological harmful agents in the workplace. Therefore, this study was designed and implemented to analyze the risk of COVID-19 based on fuzzy logic. Materials and Methods: This cross-sectional and descriptive-analytical study was conducted in 5 hospitals and health-treatment centers in Qom City, Iran (2019). The study sample included 247 employees of these medical centers. The risk assessment of COVID-19 is based on the rapid COVID-19 hazard analysis (RCHA) technique in which the risk level is calculated based on the three components of disease probability, consequence severity, and health belief level. Also, the data were analyzed using fuzzy logic. Results: The results of the fuzzy analysis of COVID-19 risk in these medical centers showed that the studied subjects were placed in five risk levels, including 10.5, 16.25, 26.75, 38.5, and 56.0. These results revealed that the group of nurses is at the highest risk of COVID-19 compared to the other seven groups working in medical centers. The definite risk of COVID-19 among people in this group was calculated at four levels equal to 16.25, 26.75, 38.5, and 56.0. Conclusion: The results of fuzzy analysis of COVID-19 risk indicated that the three groups of nurses, patient carriers, and ward services have the highest risk, respectively. Therefore, these groups should be prioritized in providing suitable solutions to prevent this disease.

Fuzzy Logic-based Risk Analysis of COVID-19 Infection: A Case Study in Healthcare Facilities Keywords:

Risk analysis , Rapid COVID-19 hazard analysis , COVID-19 , Fuzzy logic , Treatment centers