Statistics, Politics, and Policy, 2, issue 1, article 1.The article of record as published may be found at http://dx.doi.org/10.2202/2151-7509.1018The Early Aberration Reporting System (EARS) is used by some local health departments
(LHDs) to monitor emergency room and clinic data for disease outbreaks. Using actual chief
complaint data from local public health clinics, we evaluate how EARS—both the baseline system
distributed by the CDC and two variants implemented by one LHD—perform at locally detecting
the 2009 influenza A H1N1 pandemic. We also compare the EARS methods to a CUSUM-based
method. We find that the baseline EARS system performed poorly in comparison to one of the
LHD variants and the CUSUM-based method. These results suggest that changes in how
syndromes are defined can substantially improve EARS performance. The results also show that
incorporating algorithms that use more historical data will improve EARS performance for routine
surveillance by local health departments
