Sentence examples for aberration detection from inspiring English sources

Aberration detection is most needed for detecting low-to-moderate increases in cases and for slowly increasing outbreaks.

The aberrant genomic intervals in each population were determined by the aberration detection algorithm ADM2 [ 31].

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.

I will describe three ongoing research thrusts in the fields of mixed model analysis, haplotype phasing and imputation, and mosaic aberration detection, each taking place at the intersection of statistics, computer science, and biology.

A threshold method of aberration detection seems most promising for near-real-time ILI surveillance.

For such data sources with low coverage, methods other than space-time scan statistics seem more appropriate to generate useful information for public health practice (like aberration detection in time).

Although all surveillance algorithms involve aberration detection and decision making, evaluation of the uncertainty inherent in such prediction and the spatial-temporal characteristics involved has been only partially addressed.

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.

Other comparative approaches have been used or suggested (especially to compare timeliness of data sources), including cross-correlation time series modeling [23], [24], comparison of peaks [22], and comparison of aberration detection [26].

Other aberration detection methods exist, as do other simulated datasets.

We compared the performance of six aberration detection methods.