Comparing syndromic surveillance detection methods: EARS' versus a CUSUM-based methodology.

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This paper compares the performance of three detection methods, entitled C1, C2, and C3, that are implemented in the early aberration reporting system (EARS) and other syndromic surveillance systems versus the CUSUM applied to model-based prediction errors. The cumulative sum (CUSUM) performed significantly better than the EARS' methods across all of the scenarios we evaluated. These scenarios consisted of various combinations of large and small background disease incidence rates, seasonal cycles from large to small (as well as no cycle), daily effects, and various types and levels of random daily variation. This leads us to recommend replacing the C1, C2, and C3 methods in existing syndromic surveillance systems with an appropriately implemented CUSUM method.



Bioterrorism, Computer Simulation, Disease Outbreaks, Epidemiologic Methods, Humans, Population Surveillance, Public Health Informatics, Regression Analysis, Syndrome