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<p>After the terrorist attacks of September 11, 2001, questions developed over how quickly the country could respond if a bioterrorism attack was to occur. \"Syndromic surveillance\" systems are a relatively new concept that is being implemented and used by public health practitioners to attempt to detect a bioterrorism attack earlier than would be possible using conventional biosurveillance methods. The idea behind using syndromic surveillance is to detect a bioterrorist attack by monitoring potential leading indicators of an outbreak such as absenteeism from work or school, over-the-counter drug sales, or emergency room counts. The Center for Disease Control and Prevention's Early Aberration Reporting System (EARS) is one syndromic surveillance system that is currently in operation around the United States. This thesis compares the performance of three syndromic surveillance detection algorithms, entitled C1, C2, and C3, that are implemented in EARS, versus the CUSUM applied to model-based prediction errors. The CUSUM performed significantly better than the EARS' methods across all of the scenarios 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 levels of random daily variation. This results in the recommendation to replace the C1, C2, and C3 methods in existing syndromic surveillance systems with an appropriately implemented CUSUM method.
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<br>Subjects: Bioterrorism; United States; Prevention; Biosecurity</div>
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Hegler, Benjamin L.</div>
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