Comparison of Aberration Detection Algorithms for Biosurveillance Systems
نویسندگان
چکیده
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.
منابع مشابه
Research Paper: Recombinant Temporal Aberration Detection Algorithms for Enhanced Biosurveillance
OBJECTIVE Broadly, this research aims to improve the outbreak detection performance and, therefore, the cost effectiveness of automated syndromic surveillance systems by building novel, recombinant temporal aberration detection algorithms from components of previously developed detectors. METHODS This study decomposes existing temporal aberration detection algorithms into two sequential stage...
متن کاملRecombinant Temporal Aberration Detection Algorithms for Enhanced Biosurveillance
Methods: This study decomposes existing temporal aberration detection algorithms into two sequential stages and investigates the individual impact of each stage on outbreak detection performance. The data forecasting stage (Stage 1) generates predictions of time series values a certain number of time steps in the future based on historical data. The anomaly measure stage (Stage 2) compares feat...
متن کاملCategory-Specific Comparison of Univariate Alerting Methods for Biosurveillance Decision Support
Introduction Temporal alerting algorithms commonly used in syndromic surveillance systems are often adjusted for data features such as cyclic behavior but are subject to overfitting or misspecification errors when applied indiscriminately. In a project for the Armed Forces Health Surveillance Center to enable multivariate decision support, we obtained 4.5 years of outpatient, prescription and l...
متن کاملAberration Detection in R Illustrated by Danish Mortality Monitoring
The objective of biosurveillance in this chapter is the detection of emerging incidence clusters in time of a health related event. Reviews on temporal surveillance can be found e.g. in Sonesson and Bock (2003), Bravata et al. (2004), Buckeridge et al. (2005) and Tennant et al. (2007). In recent years a pleasant development has been a synthesis of surveillance methods with methods from statisti...
متن کامل