Premium
Development and evaluation of a data‐adaptive alerting algorithm for univariate temporal biosurveillance data
Author(s) -
Elbert Yevgeniy,
Burkom Howard S.
Publication year - 2009
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3708
Subject(s) - computer science , univariate , initialization , smoothing , data mining , control chart , exponential smoothing , time series , sensitivity (control systems) , series (stratigraphy) , algorithm , machine learning , multivariate statistics , paleontology , process (computing) , electronic engineering , engineering , computer vision , biology , programming language , operating system
This paper discusses further advances in making robust predictions with the Holt–Winters forecasts for a variety of syndromic time series behaviors and introduces a control‐chart detection approach based on these forecasts. Using three collections of time series data, we compare biosurveillance alerting methods with quantified measures of forecast agreement, signal sensitivity, and time‐to‐detect. The study presents practical rules for initialization and parameterization of biosurveillance time series. Several outbreak scenarios are used for detection comparison. We derive an alerting algorithm from forecasts using Holt–Winters‐generalized smoothing for prospective application to daily syndromic time series. The derived algorithm is compared with simple control‐chart adaptations and to more computationally intensive regression modeling methods. The comparisons are conducted on background data from both authentic and simulated data streams. Both types of background data include time series that vary widely by both mean value and cyclic or seasonal behavior. Plausible, simulated signals are added to the background data for detection performance testing at signal strengths calculated to be neither too easy nor too hard to separate the compared methods. Results show that both the sensitivity and the timeliness of the Holt–Winters‐based algorithm proved to be comparable or superior to that of the more traditional prediction methods used for syndromic surveillance. Copyright © 2009 John Wiley & Sons, Ltd.