Background: Emergency department (ED) based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understanding of the normal patterns of healthcare usage. Unfortunately, systematic methods for determining the expected number of (ED) visits on a particular day have not yet been well established. We present here a generalized methodology for developing models of expected ED visit rates.
For instance, using time series methods, the authors in REF develop some models of emergency department utilization for identifying abnormally high visit rates that may be an early warning of a bioterrorist attack.
Time series modeling for syndromic surveillance
BMC Medical Informatics and Decision Making
BMC Medical Informatics and Decision Making
Medicine Computer Science