These modifi cations were apparently effective for the following reasons. Accounting for total visits to the facility (i.e., rate method) produces a more accurate expected value and lower residuals ( Table 2 ). Although number of total visits is not the ideal denominator, in general it is better than no denominator at all. An advantage of the rate method is that calculations may be made when only partial data for a given day are available. However, adjusting for total visits may reduce sensitivity slightly in some subgroups, as we found for the hospital ED data when the mean count was 8 to <20. Stratifi cation by weekday versus weekend day improves expected value calculations when a substantial day-of-week effect exists, such as in the DoD data. When such an effect is not present, stratifi cation causes days further from the index day to be used in the baseline period, therefore producing slightly less accurate expected values. Longer baseline durations have no effect on the accuracy of expected value calculation and improve sensitivity by producing more accurate and stable SD values. Using a higher minimum SD avoids nuisance alerts that may be prompted by small fl uctuations in the daily visit count. This method also changes the distribution of test statistic values, which results in a lower 99th percentile cutoff value, which increases sensitivity for detecting moderate-to-high numbers of added counts. Using a higher minimum SD is benefi cial if disease indicators with low and high counts are analyzed; an alternate approach is to use different methods for lowversus high-count data. The issues focused on by our suggested modifi cations may alternately be addressed by various sophisticated mathematical modeling approaches. However, health departments, which are generally limited in resources and in analysis expertise, may resist use of decision-support methods that are expensive, diffi cult to implement, or not transparent to human data monitors. For example, sophisticated Serfl ing-type regression models have long been used by CDC for tracking the progress of infl uenza season (13, 14) and have been used to analyze selected data in the Bio-Sense system. However, these models have both strengths and weaknesses and have not been widely embraced for daily disease surveillance. Even if the expertise and hardware capability for applying them were made available to local health departments, many time series are unsuitable for this approach. We present simple and easily understood and implemented enhancements to C2 to extend its applicability and improve its performance. These enhancements may be applicable to other control chart-based algorithms as well.