Computer simulation is important in vaccination policy analysis. It is really the only method available for evaluating vaccination policy for diseases that are rare (e.g., smallpox) or emergency vaccinations (e.g., 2009 H1N1 pandemic). The most realistic method for computer simulation of vaccination policy is called agent-based simulation (ABS). In an ABS, agents have similar socio-demographic characteristics to a population of interest. Currently, we use published information about vaccine efficacy (VE) as the probability that an agent develops immunity as a result of being vaccinated. However, published results of VE trials typically report a single overall VE. Thus, ABS’s potential to realistically simulate the effects of co-existing diseases, gender and other characteristics of a population is under-used. In this paper, we evaluate a Bayesian Network (BN) model of a VE trial dataset as a compact representation of the dataset. From the BN model, it will be possible to obtain VEs conditioned on an agent's characteristics. We expect that an improvement in information about VE needed for vaccination policy analysis will lead to more effective use of vaccines, and ultimately improvements in health.