Health care officials are increasingly concerned with knowing early whether an outbreak of a particular disease is unfolding. We often have daily counts of some variable that are indicative of the number of individuals in a given community becoming sick each day with a particular disease. By monitoring these daily counts we can possibly detect an outbreak in an early stage. A number of classical time-series methods have been applied to outbreak detection based on monitoring daily counts of some variables. These classical methods only give us an alert as to whether there may be an outbreak. They do not predict properties of the outbreak such as its size, duration, and how far we are into the outbreak. Knowing the probable values of these variables can help guide us to a cost-effective decision that maximizes expected utility. Bayesian networks have become one of the most prominent architectures for reasoning under uncertainty in artificial intelligence. We present an intelligent system, implemented using a Bayesian network, which not only detects an outbreak, but predicts its size and duration, and estimates how far we are into the outbreak. We show results of investigating the performance of the system using simulated outbreaks based on real outbreak data. These results indicate that the system shows promise of being able to predict properties of an outbreak.