The C2 algorithm uses a sliding baseline of 7 consecutive recent days' counts to calculate a mean (μ) and SD (s t ). The test statistic is (x t -μ)/s t , the number of SDs by which the current value x t exceeds μ, or 0 if x t does not exceed μ. EARS uses a test statistic >3 to signal an alert (6, 7) . Owing to their simplicity, ease of implementation, and implicit correction for seasonal trends (only data from the prior 9 days are used), the EARS algorithms are widely used (8) (9) (10) . However, the algorithms do not perform optimally under all circumstances. First, because daily counts often vary by day of week, many alerts may be produced on high-count days such as Mondays and Tuesdays, and few may be produced on low-count days such as weekend days. Second, the short (7-day) baseline period may produce unstable values for the mean and SD; thus, the minimum daily count that triggers an alert may vary widely over a short period. Third, using simple count data does not account for the population at risk, which is generally unknown in these systems and which may vary, especially during crisis situations. Although C2 can be used on rates rather than counts, prior evaluations have not shown that using rates improves performance (L. Hutwagner, pers. comm.). Finally, occurrences of many disease indicators are rare, resulting in calculations for both expected values and SDs of 0; the EARS methods are not recommended in such instances. A minimum SD may be used to avoid division by zero, but if this minimum value is set to 0.2, a count of 1 will be 5 SDs above the mean and trigger a high-level alert.