Pervasive human and organizational factors (HOFs) within the public sectors play a vital role in the prevention and control of epidemic (PCE). Insufficient analysis of HOFs has helped continue the use of flawed precautions. In this study, we attempted to establish a quantitative model to (a) clarify HOFs within the public sectors with regard to PCE, (b) predict the probability of relevant risk factors and an epidemic, and (c) diagnose the critical factors. First, we systematically identified 47 HOFs based on the Human Factors Analysis and Classification System (HFACS). We then converted the HFACS framework into a Bayesian Network (BN) after determining the causalities among these factors. Finally, we applied the hybrid HFACS-BN model to analyze the COVID-19 outbreak in China by virtue of its efficacy in probability prediction and diagnosis of key risk factors, and thus to test the feasibility of the model itself. This study contributes to a holistic analysis of HOFs within the public sectors with regard to PCE by providing a risk assessment model for epidemics or pandemics, and developing risk analysis methods for the public health field.