Likelihood assessment of major urban public health emergencies based on an improved fuzzy Bayesian network

Preventing and controlling major public health emergencies is a prerequisite for ensuring high-quality urban development. Likelihood assessment is crucial for the prevention and control of major urban public health emergencies. This study uses an improved fuzzy Bayesian network (FBN) as an analytical tool to assess the likelihood of a major urban public health emergency within the overall framework of the public health governance system. In the assessment process, the interrelationship among factors affecting the likelihood is considered, uncertainties are captured and quantified, and expert opinions are scientifically aggregated to make the assessment results more reasonable. The assessment process is illustrated using the COVID-19 pandemic in Wuhan as an example. In addition, the improved FBN is used to examine the impact of main influencing factors on the likelihood of major urban public health emergencies. Results show that the likelihood of the COVID-19 pandemic in Wuhan is very high. Factors such as information sharing, coordination and cooperation among various personnel, effectiveness of quarantine and isolation measures significantly influence the likelihood. Strengthening public health governance and preventing escalation in the critical window are beneficial for reducing the likelihood. This study advances understanding of the prevention of major urban public health emergencies amid current public health governance challenges and facilitates decision-making for abating and defusing major urban public health emergencies.