Introduction: Certain modeling techniques have been implemented to detect unusually high emergency department (ED) visit rates on the basis of historical data, including parametric regression, autoregression, multiresolution wavelet analysis, and additive modeling. An outbreak-detection strategy is informative only to the extent that its specificity is known because the probability of a false alarm in the absence of an outbreak should be understood to allocate resources appropriately in the event of an alert that triggers a public health investigation.

Objectives: This study demonstrated that the specificity of outbreak detection using current methods varies substantially on multiple timescales. A modeling approach that provides constant specificity surveillance was developed.

Methods: Autoregressive, Serfling, trimmed seasonal, and wavelet-based outbreak detection models were evaluated for changes in specificity over time. All model simulations used 12 years of historical respiratory syndrome ED visits at a major pediatric hospital in an urban setting. Changes in specificity were detected by using error analyses modified for binomial data and chi-squared analysis. Sensitivity was evaluated by adding synthetic 1-day outbreaks among 10 patients to the historical data. A new outbreak-detection method was developed that used generalized additive models of both the ED visit mean and variance.

Results: The specificity of four previously published models (i.e., autoregressive, Serfling, trimmed seasonal, and wavelet-based) was a nonconstant function of the day of the week, the month of the year, or the year of the study (p<0.05). The seasonal changes in specificity led to a paradoxical increase in sensitivity to simulated outbreaks during winter months when compared with summer months. The new method had constant specificity over all three time scales (p<0.05) without a loss of sensitivity compared with previous models.