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
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.