monitoring small-or intermediate-sized outbreaks or illnesses defi ned primarily by laboratory testing. Even when using these suggested modifi cations, sensitivity for detecting additional counts at the facility level remains modest. However, the utility of automated biosurveillance will be expanded with the availability of better population coverage and more specifi c data, the use of multiple data types in combination, and improved detection algorithms, such as those proposed here. The limitations of this study include using only data with a mean count >0.5 per day; analyses of sparser data might show different results. We studied only facility-level aggregation of data, selected patient types (e.g., hospital inpatients were not studied), selected data types (e.g., ED diagnoses were not studied), and broadly defi ned syndromes (the more granular subsyndromes, which are likely to yield lower counts, were not studied). Although we evaluated only a simple time-series detection method, optimizing performance of simple methods is useful before they can be meaningfully compared with more sophisticated methods, such as regression. Also, we studied effects of additional counts on single days rather than multiday outbreak effects; however, because the C2 algorithm considers data from only 1 day at a time, this is a reasonable initial approach. These results must be confi rmed by trials of multiday signal injection and performance evaluated for multiple subgroups (e.g., syndrome, day of week, season). We adopted the approach of evaluating sensitivity at a fi xed 1% alert rate defi ned empirically for each algorithm and dataset, as used by Jackson et al. (12) . Our approach is in accord with a recent review that recommended basing alert thresholds on empirical data rather than on classical statistical theory (17) . A major strength of the study is that BioSense is a national system that provided access to 2 major datasets with differing characteristics and to data from hundreds of facilities in many states. The length, geographic spread, and syndrome variation of the study datasets lend weight to the results.