Practical comparison of aberration detection algorithms for biosurveillance systems.
National syndromic surveillance systems require optimal anomaly detection methods. For method performance comparison, we injected multi-day signals stochastically drawn from lognormal distributions into time series of aggregated daily visit counts from the U.S. Centers for Disease Control and Prevention's BioSense syndromic surveillance system. The time series corresponded to three different syndrome groups: rash, upper respiratory infection, and gastrointestinal illness. We included a sample of facilities with data reported every day and with median daily syndromic counts ⩾1 over the entire study period. We compared anomaly detection methods of five control chart adaptations, a linear regression model and a Poisson regression model. We assessed sensitivity and timeliness of these methods for detection of multi-day signals. At a daily background alert rate of 1% and 2%, the sensitivities and timeliness ranged from 24 to 77% and 3.3 to 6.1days, respectively. The overall sensitivity and timeliness increased substantially after stratification by weekday versus weekend and holiday. Adjusting the baseline syndromic count by the total number of facility visits gave consistently improved sensitivity and timeliness without stratification, but it provided better performance when combined with stratification. The daily syndrome/total-visit proportion method did not improve the performance. In general, alerting based on linear regression outperformed control chart based methods. A Poisson regression model obtained the best sensitivity in the series with high-count data.
Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA 30333, United States. Electronic address: fwd6@cdc.gov.
Hong
H
Zhou
Johns Hopkins Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, United States.
Howard
H
Burkom
Veterans Health Administration, Office of Public Health Surveillance and Research, 3801 Miranda Avenue (132), Palo Alto, CA 94304, United States.
Carla A
CA
Winston
Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA 30333, United States.
Achintya
A
Dey
Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA 30333, United States.
Umed
U
Ajani
D000465:Algorithms; D055877:Biosurveillance; D002487:Centers for Disease Control and Prevention, U.S.; D004196:Disease Outbreaks; D016014:Linear Models; D011159:Population Surveillance; D012680:Sensitivity and Specificity; D014481:United States
Aberration detection; Algorithm; Biosurveillance; Control chart; Poisson regression