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The results of this study suggest that commonly-used aberration detection methods for syndromic surveillance often do not perform well in detecting signals other than large, rapid increases in case counts relative to baseline levels.
Other aberration detection methods exist, as do other simulated datasets.
We compared the performance of six aberration detection methods.
Many studies have described the performance of individual aberration detection methods [ 3- 10].
Several alternative aberration detection methods are available to state and local health departments for syndromic surveillance.
This lack of comparisons on actual syndromic surveillance data makes it difficult to select aberration detection methods objectively.
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Penetrance of aberrant chromosomal areas across the genome was demonstrated using Aberration Detection Method 2 (Agilent Genomic Workbench Lite Edition 6.5.0.18, Agilent Technologies), a quality-weighted interval score algorithm that identifies aberrant intervals in samples that have consistent gain or loss log ratios based on their statistical score.
On reviewing the literature on influenza surveillance, Dailey et al. [26] concluded that an aberration detection method (e.g., use of a threshold, CUSUM, or scan statistic) was preferable to both comparison of peaks and correlation.
We used the Aberration Detection Method 2 (ADM-2) algorithm to identify DNA copy number aberrations.
Copy number aberration was indicated using the Aberration Detection Method 2 (ADM-2) algorithm.
The Aberration Detection Method 2 algorithm with threshold at 6 (Agilent Genomic Workbench) was applied to identify common genomic aberrations.
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