Identification of Key Factors in Global Public Health Safety Assessment Based on Bayesian Belief Networks During the COVID‐19 Pandemic

Risk factors at different stages of COVID-19 may interact with each other, forming a risk network. Identifying the key risk factors within this network and their interrelationships is crucial for reducing the overall risk of COVID-19. We constructed three Bayesian Belief Network (BBN) models by combining data-driven approaches with expert validation. Using the Tree-Augmented Naive Bayes (TAN) algorithm, we developed the INFORM COVID-19 Risk BBN model and the COVID-19 Regional Safety Assessment BBN model. The joint BBN model was established using the Greedy Thick Thinning (GTT) algorithm. Parameter learning was performed through maximum likelihood estimation. Expert validation, 10-fold cross-validation, and model performance metrics were employed to comprehensively assess the overall performance of the models. Additionally, mutual information analysis and sensitivity analysis were used to explore the importance of risk factors at each stage and their interdependencies. "INFORM Vulnerability" and "INFORM Lack of Coping Capacity" were identified as the two key risk factors influencing the risk of early outbreak. In the mid-to-late stages of the pandemic, "Emergency Preparedness" and "Monitoring and Detection" had the greatest impact on regional safety and control measures. Furthermore, the joint BBN model indicated that the most important risk factors affecting the overall COVID-19 risk were "Lack of Coping Capacity," "Government Risk Management Efficiency," and "Regional Resiliency," while the influence of other variables was relatively minor. The main contribution of this study lies in identifying the key risk factors at different stages of the pandemic and their interdependencies, providing policymakers with valuable insights for the rational allocation of limited health resources and the formulation of appropriate and effective prevention and control policies.