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Reducing false alarms in syndromic surveillance
Author(s) -
Peter William,
Najmi Amir H.,
Burkom Howard S.
Publication year - 2011
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4204
Subject(s) - computer science , biostatistics , false alarm , public health surveillance , data mining , constant false alarm rate , public health , noise (video) , channel (broadcasting) , artificial intelligence , medicine , telecommunications , nursing , image (mathematics)
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.