Our results demonstrate that simple modifi cations of the widely used C2 algorithm can substantially improve the ability to accurately recognize 1-day increases in disease syndrome activity. Depending on the dataset, mean count in the data, and the number of additional counts added, the enhanced methods may increase sensitivity by 20%-40%. These improvements were achieved without an increase in the alert rate, which was held constant at 1% for all methods. Although we chose a 1% alert rate for testing purposes, in practice, it is useful to vary the alert rate to fi t the circumstances, and the BioSense application enables the alert rate to be varied between 0.1% and 2%. Regardless of the alert rate used, the modifi ed methods have higher sensitivity. For the DoD and hospital ED datasets, sensitivity was improved by using a higher minimum SD of 1.0, a longer baseline duration of 28 days, and adjusting for total visits. Stratifying baseline days into weekdays versus weekends/ holidays increased sensitivity in the DoD data, which has a strong day-of-week effect, but modestly decreased sensitivity in the hospital ED data, which does not have such an effect. Thus, the best analytic methods depend on dataset characteristics, especially the day-of-week effect, and could be varied by manual or automated selection. These fi ndings can be used to improve both early event detection and situation awareness because accurate recognition of unusually high counts is needed for both uses.