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Issue No.04 - April (2010 vol.22)

pp: 565-577

Hsin-Min Lu , University of Arizona, Tucson

Daniel Zeng , University of Arizona, Tucson and the Chinese Academy of Sciences

Hsinchun Chen , University of Arizona, Tucson

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.115

ABSTRACT

Accurate and timely detection of infectious disease outbreaks provides valuable information which can enable public health officials to respond to major public health threats in a timely fashion. However, disease outbreaks are often not directly observable. For surveillance systems used to detect outbreaks, noises caused by routine behavioral patterns and by special events can further complicate the detection task. Most existing detection methods combine a time series filtering procedure followed by a statistical surveillance method. The performance of this "two-step” detection method is hampered by the unrealistic assumption that the training data are outbreak-free. Moreover, existing approaches are sensitive to extreme values, which are common in real-world data sets. We considered the problem of identifying outbreak patterns in a syndrome count time series using Markov switching models. The disease outbreak states are modeled as hidden state variables which control the observed time series. A jump component is introduced to absorb sporadic extreme values that may otherwise weaken the ability to detect slow-moving disease outbreaks. Our approach outperformed several state-of-the-art detection methods in terms of detection sensitivity using both simulated and real-world data.

INDEX TERMS

Markov switching models, syndromic surveillance, Gibbs sampling, outbreak detection.

CITATION

Hsin-Min Lu, Daniel Zeng, Hsinchun Chen, "Prospective Infectious Disease Outbreak Detection Using Markov Switching Models",

*IEEE Transactions on Knowledge & Data Engineering*, vol.22, no. 4, pp. 565-577, April 2010, doi:10.1109/TKDE.2009.115REFERENCES

- [4] O. Ivanov, M.M. Wagner, W.W. Chapman, and R.T. Olszewski, "Accuracy of Three Classifiers of Acute Gastrointestinal Syndrome for Syndromic Surveillance,"
Proc. Am. Medical Informatics Assoc. (AMIA) Symp., pp. 345-349, 2002.- [6] J. Espino, M. Wagner, F. Tsui, H. Su, R. Olszewski, Z. Lie, W. Chapman, X. Zeng, L. Ma, Z. Lu, and J. Dara, "The RODS Open Source Project: Removing a Barrier to Syndromic Surveillance,"
Studies in Health Technology and Informatics, vol. 107, pp. 1192-1196, 2004.- [8] B.Y. Reis, M. Pagano, and K.D. Mandl, "Using Temporal Context to Improve Biosurveillance,"
Proc. Nat'l Academy of Sciences USA, vol. 100, pp. 1961-1965, Feb. 2003.- [9] B.Y. Reis and K.D. Mandl, "Time Series Modeling for Syndromic Surveillance,"
BMC Medical Informatics and Decision Making, vol. 3, no. 2, Jan. 2003.- [11] W.A. Shewhart,
Statistical Method from the Viewpoint of Quality Control. Department of Agriculture, The Graduate School, 1939.- [12] D.C. Montgomery,
Introduction to Statistical Quality Control, fifth ed. Wiley, 2005.- [13] E.S. Page, "Continuous Inspection Schemes,"
Biometrika, vol. 41, nos. 1/2, pp. 100-115, June 1954.- [14] CDC, "Increased Antiviral Medication Sales Before the 2005-06 Influenza Season-New York City,"
Morbidity and Mortality Weekly Report, vol. 55, pp. 277-279, Mar. 2006.- [15] D.L. Buckeridge, P. Switzer, D. Owens, D. Siegrist, J. Pavlin, and M. Musen, "An Evaluation Model for Syndromic Surveillance: Assessing the Performance of a Temporal Algorithm,"
Morbidity and Mortality Weekly Report, vol. 54, pp. 109-115, Aug. 2005.- [16] M.P. Clements and D.F. Hendry, "Forecasting with Breaks,"
Handbook of Economic Forecasting, vol. 1, pp. 605-657, Elsevier, 2006.- [19] B.Y. Reis and K.D. Mandl, "Integrating Syndromic Surveillance Data Across Multiple Locations: Effects on Outbreak Detection Performance,"
Proc. Am. Medical Informatics Assoc. (AMIA) 2003 Symp., pp. 549-553, 2003.- [20] G. Box and G. Jenkins,
Time Series Analysis: Forecasting and Control. Holden Day, 1970.- [21] W.H. Greene,
Econometric Analysis. Prentice Hall, 2000.- [23] H. Akaike, "Information Theory and an Extension of the Likelihood Principle,"
Proc. Second Int'l Symp. Information Theory, B.N. Perov and F. Csaki, eds., 1973.- [24] G. Schwarz, "Estimating the Dimension of a Model,"
Annals of Statistics, vol. 6, pp. 461-464, 1978.- [25] C.M. Bishop,
Pattern Recognition and Machine Learning. Springer, 2006.- [26] H. White,
Approximate Nonlinear Forecasting Methods, vol. 1, chapter 9, pp. 459-512. Elsevier, Jan. 2006.- [27] J. Shao, "An Asymptotic Theory for Linear Model Selection,"
Statistica Sinica, vol. 7, pp. 221-264, 1997.- [28] M.L. Jackson, A. Baer, I. Painter, and J. Duchin, "A Simulation Study Comparing Aberration Detection Algorithms for Syndromic Surveillance,"
BMC Medical Informatics and Decision Making, vol. 7, no. 6, 2007.- [29] S.C. Wieland, J.S. Brownstein, B. Berger, and K.D. Mandl, "Automated Real Time Constant-Specificity Surveillance for Disease Outbreaks,"
BMC Medical Informatics and Decision Making, vol. 7, no. 15, 2007.- [30] J. Zhang, F.-C. Tsui, M.M.W. William, and R. Hogan, "Detection of Outbreaks from Time Series Data Using Wavelet Transform,"
Proc. Am. Medical Informatics Assoc. (AMIA) Symp., 2003.- [31] R. Serfling, "Methods for Current Statistical Analysis of Excess Pneumonia-Influenza Deaths,"
Public Health Reports, vol. 78, pp. 494-506, 1963.- [32] J.C. Brillman, T. Burr, D. Forslund, E. Joyce, R. Picard, and E. Umland, "Modeling Emergency Department Visit Patterns for Infectious Disease Complaints: Results and Application to Disease Surveillance,"
BMC Medical Informatics and Decision Making, vol. 5, no. 4, 2005.- [36] J. Hamilton,
Time Series Analysis. Princeton, 1994.- [37] A.N. Shiryaev, "On Optimum Methods in Quickest Detection Problems,"
Theory of Probability and Its Applications, vol. 8, pp. 22-46, 1963.- [39] M. Frisen and J. De Mare, "Optimal Surveillance,"
Biometrika, vol. 78, no. 2, pp. 271-280, 1991.- [42] M. Frisen, "Statistical Surveillance. Optimality and Methods,"
Int'l Statistical Rev., vol. 71, no. 2, pp. 403-434, 2003.- [44] S.H. Steiner, "EWMA Control Charts with Time-Varying Control Limits and Fast Initial Response,"
J. Quality Technology, vol. 31, no. 1, pp. 75-86, 1999.- [46] H. Burkom, "Alerting Algorithms for Biosurveillance,"
Disease Surveillance: A Public Health Informatics Approach, pp. 143-192, John Wiley & Sons, 2007.- [47] C.-J. Kim and C.R. Nelson,
State-Space Models with Regime Switching. MIT Press, 1999.- [53] A.P. Dempster, N.M. Laird, and D.B. Rubin, "Maximum Likelihood from Incomplete Data via the EM Algorithm,"
J. Royal Statistical Soc., Series B (Methodological), vol. 39, no. 1, pp. 1-38, 1977.- [59] D. Madigan, "Bayesian Data Mining for Health Surveillance,"
Spatial and Syndromic Surveillance for Public Health, pp. 203-221, John Wiley & Sons, 2005.- [60] J. Besag, "Spatial Interaction and the Statistical Analysis of Lattice Systems,"
J. Royal Statistical Soc., Series B (Methodological), vol. 36, no. 2, pp. 192-236, 1974.- [61] S. Geman and D. Geman, "Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images,"
IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, no. 6, pp. 721-741, Nov. 1984.- [62] ISDS, "My Algorithm Can Out-Detect Your Algorithm: Biosurveillance Using Time Series Data," technical report, Int'l Soc. for Disease Surveillance, https://wiki.cirg.washington.edu/pub/bin/ view/IsdsTechnicalContest; accessed, Nov. 2008.
- [63] L.M. Wein, D.L. Craft, and E.H. Kaplan, "Emergency Response to an Anthrax Attack,"
Proc. Nat'l Academy of Sciences USA, vol. 100, pp. 4346-4351, Apr. 2003.- [65] T. Burr, T. Graves, R. Klamann, S. Michalak, R. Picard, and N. Hengartner, "Accounting for Seasonal Patterns in Syndromic Surveillance Data for Outbreak Detection,"
BMC Medical Informatics and Decision Making, vol. 6, no. 40, 2006. |