To date, most syndromic surveillance systems rely heavily on complicated statistical algorithms to identify aberrations. The assumption is that when the statistics identify something unusual, follow-up should occur. However, with multiple strata analyzed, small numbers for some strata, and wide variances in daily counts, the statistical algorithms will generate flags too often. Experience has shown that these flags usually have little or no public health significance. As a result, syndromic surveillance systems suffer from the ‘boy who cried wolf’ syndrome. It is clear that the analyst’s ability to use professional judgment to sift through multitudes of flags is very important to the success of the system, which suggests that statistics alone cannot identify issues of public health importance from ED data.
Objective
This study's aim was to refine an automated biosurveillance system in order to better suit the daily monitoring capabilities and resources of a health department.