Reducing false alarms in syndromic surveillance

William Peter, Amir H. Najmi, Howard S. Burkom

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1665-1677
Number of pages13
JournalStatistics in Medicine
Issue number14
StatePublished - Jun 30 2011
Externally publishedYes


  • Biosurveillance
  • Information theory
  • Public health
  • Syndromic counts

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


Dive into the research topics of 'Reducing false alarms in syndromic surveillance'. Together they form a unique fingerprint.

Cite this