Abstract
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 a prediction of the value of the time series a certain number of time steps in the future based on historical data. The anomaly measure stage (stage 2) compares one or more features of this prediction to the actual time series to compute a measure of the potential anomaly. This decomposition was found not only to yield valuable insight into the effects of the aberration detection algorithms but also to produce novel combinations of data forecasters and anomaly measures with enhanced detection performance.
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References
Hutwagner, L., et al.: Comparing aberration detection methods with simulated data. Emerg Infect Dis, (Feb. 2005), Available from http://www.cdc.gov/ncidod/EID/vol11no02/04-0587.htm
Reis, B.Y., Mandl, K.D.: Time series modeling for syndromic surveillance. BMC Medical Informatics and Decision Making 3(2) (2003)
Reis, B.Y., Pagano, M., Mandl, K.D.: Using temporal context to improve biosurveillance (published online before print as 10.1073/pnas.0335026100). PNAS 100, 1961–1965 (2003)
Brillman, J.C., et al.: Modeling emergency department visit patterns for infectious disease complaints: results and application to disease surveillance. BMC Medical Informatics and Decision Making 5(4), 1–14 (2005), http://www.biomedcentral.com/content/pdf/1472-6947-5-4.pdf
Wallenstein, S., Naus, J.: Scan Statistics for Temporal Surveillance for Biologic Terrorism, http://www.cdc.gov/MMWR/preview/mmwrhtml/su5301a17.htm
Chatfield, C.: The Holt-Winters Forecasting Procedure. App. Stats. 27, 264–279 (1978)
Chatfield, C., Yar, M.: Holt-Winters Forecasting: Some Practical Issues. The Statistician 37, 129–140 (1988)
Siegrist, D., Pavlin, J.: BioALIRT biosurveillance detection algorithm evaluation. Syndromic Surveillance: Reports from a National Conference, 2003. MMWR 53(Suppl.), 152–158 (2004)
Sartwell, P.E.: The distribution of incubation periods of infectious disease. Am. J. Hyg. 51, 310–318 (1950)
Burkom, H.S., Murphy, S.P., Shmueli, G.: Automated Time Series Forecasting for Biosurveillance. Statistics in Medicine (accepted for 2007 publication)
Burkom, H.S.: Development, Adaptation, and Assessment of Alerting Algorithms for Biosurveillance. Johns Hopkins APL Technical Digest 24(4), 335–342 (2003)
Tokars, J., Bloom, S.: The Predictive Accuracy of Non-Regression Data Analysis Methods. In: Poster, 2006 Syndromic Surveillance Conference, Baltimore, MD, October 2006, pp. 19–20 (2006)
Lucas, J.M., Crosier, R.B.: Fast Initial Response for CuSum Quality-Control Schemes: Give Your CuSum a Head Start. Technometrics 24, 199–205 (1984)
Muscatello, D.: An adjusted cumulative sum for count data with day-of-week effects: application to influenza-like illness. In: 2004 Syn. Surv. Conf., Boston, MA, Nov. 3-4 (2004)
Lombardo, J.S., et al.: A Systems Overview of the Electronic Surveillance System for the Early Notification of Community-Based Epidemics. Journal of Urban Health (Proceedings of the 2002 National Syndromic Surveillance Conference) 80(2), Suppl. 1, i32–i42 (2003)
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Murphy, S., Burkom, H. (2007). Decoupling Temporal Aberration Detection Algorithms for Enhanced Biosurveillance. In: Zeng, D., et al. Intelligence and Security Informatics: Biosurveillance. BioSurveillance 2007. Lecture Notes in Computer Science, vol 4506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72608-1_10
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DOI: https://doi.org/10.1007/978-3-540-72608-1_10
Publisher Name: Springer, Berlin, Heidelberg
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