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RISK ASSESSMENT FOR COVID-19 TRANSMISSION IN A HEALTH CARE CENTRE BASED ON FAULT TREE AND EVENT TREE

dc.contributor.authorChe, Yijun
dc.contributor.copyright-releaseNot Applicableen_US
dc.contributor.degreeMaster of Applied Scienceen_US
dc.contributor.departmentDepartment of Industrial Engineeringen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.external-examinerPaul Amyotteen_US
dc.contributor.external-examinerMohammad Alauddinen_US
dc.contributor.graduate-coordinatorJohn Blakeen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.thesis-readerFloris Goerlandten_US
dc.contributor.thesis-supervisorPeter VanBerkelen_US
dc.date.accessioned2022-12-16T12:45:32Z
dc.date.available2022-12-16T12:45:32Z
dc.date.defence2022-12-08
dc.date.issued2022-12-15
dc.description.abstractHealthcare systems face significant challenges in containing the spread of infectious diseases. This work applies risk assessment and risk management, to evaluate COVID-19 transmission in the healthcare system. The starting point of this research is a Bow-Tie analysis that identified virus threats and consequences of the COVID-19 pandemic for various receptor groups. The study was motivated by the need to assess barriers to transmission in a quantitative manner. This work proposed a quantitative framework for stakeholders to analyze transmission risk and barrier criticality based on Fault Trees and Event Trees. Using the framework, this study collected data on failure probabilities relevant to COVID-19 from journals and expert opinions for a case study. This work also conducted a qualitative assessment and sensitivity analysis of the data. The outputs of calculations based on the Fault Tree and the Event Tree provided numeric results for the probability of each threat and consequence, indicating that the highest risk of infection came from asymptomatic patients. Finally, the work evaluated the overall risk of pandemic transmission at the partner hospital using the As Low As Reasonable Practical approach. The framework is shown to be effective at quantitatively assessing the risk of COVID-19 transmission in the healthcare system.en_US
dc.identifier.urihttp://hdl.handle.net/10222/82157
dc.language.isoenen_US
dc.subjectRISK ASSESSMENTen_US
dc.subjectFAULT TREEen_US
dc.subjectEVENT TREEen_US
dc.titleRISK ASSESSMENT FOR COVID-19 TRANSMISSION IN A HEALTH CARE CENTRE BASED ON FAULT TREE AND EVENT TREEen_US
dc.typeThesisen_US

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