In this paper we examine some of the methodologies implemented by the Centers for Disease Control and Prevention's (CDC) BioSense program. The program uses data from hospitals and public health departments to detect outbreaks using the Early Aberration Reporting System (EARS). The EARS method W2 allows one to monitor syndrome counts (W2count) from each source and the proportion of counts of a particular syndrome relative to the total number of visits (W2rate). We investigate the performance of these methods, which are designed using an empiric recurrence interval (RI), with simulated parametric data. Counts from the Poisson and negative binomial distributions are generated, and used to examine W2 properties. An adaptive threshold monitoring method is introduced based on fitting sample data to the above distributions, then converting the current value to a Z-score through a p-value. We compare the thresholds required to obtain given values of the RI for different sets of parameter values. We then simulate 1-week outbreaks in our data and calculate the proportion of times these methods correctly signal an outbreak using Shewhart and exponentially weighted moving average (EWMA) charts. Our results indicate that the adaptive threshold method gives more consistent statistical performance across different parameter sets and amounts of baseline historical data used for computing the statistics. For the sensitivity analysis, the EWMA chart is superior to its Shewhart counterpart in nearly all cases and the adaptive threshold methods tend to outperform the W2 methods. Copyright © 2011 John Wiley & Sons, Ltd.