Algorithms for identifying public health threats or disease outbreaks are vulnerable to false alarms arising from sudden shifts in health‐care utilization or data participation. This paper describes a method of reducing false alerts in automated public health surveillance algorithms, and in particular, automated syndromic surveillance algorithms, that rely on health‐care utilization data. The technique is based on monitoring syndromic counts with reference to a suitable background, or reference, series of counts. The suitability of the background time series in decreasing the false‐alarm rate will be shown to be related mathematically to the so‐called mutual information that exists between the random variables representing the syndromic and background time series of counts. The method can be understood as a noise cancellation filter technique in which one noisy (reference) channel is used to cancel the background noise of the monitored (measured) channel. The issues discussed here may also be relevant to the appropriate use of rates in epidemiology and biostatistics. Copyright © 2011 John Wiley & Sons, Ltd.

utomated systems for public health surveillance have evolved over the past several years as national and local institutions have been learning the most effective ways to share and apply population health information for outbreak investigation and tracking. The changes have included developments in algorithmic alerting methodology. This article presents research efforts at The Johns Hopkins University Applied Physics Laboratory for advancing this methodology. The analytic methods presented cover outcome variable selection, background estimation, determination of anomalies for alerting, and practical evaluation of detection performance. The methods and measures are adapted from information theory, signal processing, financial forecasting, and radar engineering for effective use in the biosurveillance data environment. Examples are restricted to univariate algorithms for daily time series of syndromic data, with discussion of future generalization and enhancement.

An approach to identifying public health threats by characterizing syndromic surveillance data in terms of its surprisability is discussed. Surprisability in our model is measured by assigning a probability distribution to a time series, and then calculating its entropy, leading to a straightforward designation of an alert. Initial application of our method is to investigate the applicability of using suitably-normalized syndromic counts (i.e., proportions) to improve early event detection.

OBJECTIVE This paper describes a method of avoiding false alerts in automated syndromic surveillance algorithms which monitor the temporal relationship between a particular monitored syndrome (the "target") in rela- tionship to other reference healthcare data streams (the "context"). BACKGROUND The identification of public health threats or disease outbreaks relies on opportunistic data streams that vary greatly across geographic regions, time periods, and other factors (1). Most surveillance systems are vulnerable to dramatic and unpredictable shifts in the healthcare data they monitor (2) since anomalous counts in syndromic data streams can be biased by changes in data collection methods, seasonality, etc. For example, a sudden jump in influenza-like illness diagnoses in a population might be caused by an increase in the monitored population size.