Methodology Construction of Bayesian Network for Emerging Infectious Disease Risk Assessment

Emerging infectious diseases present substantial threats to global public health, requiring strong risk assessment methods. This study introduces a Bayesian Network (BN)-based framework for building risk assessment models for such diseases. The approach consists of three main stages: variable identification, network structure learning, and parameter estimation. Data from a hypothetical regional outbreak of "Novel Virus X" in 2024, comprising 500 confirmed cases and 1000 control samples, validated the framework. Structural learning applied the K2 algorithm with prior expert knowledge, and parameter learning used maximum likelihood estimation. A validation dataset showed that the BN model reached an 89.2% accuracy in forecasting high-risk transmission scenarios, surpassing traditional logistic regression (81.5%) and decision tree (83.7%) models. The study underscores the efficacy of BNs in merging diverse data and measuring uncertain relationships, offering a structured method for real-time risk evaluation of emerging infectious diseases. This framework can help public health officials prioritize interventions and allocate resources more efficiently.