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Naval Postgraduate School
Monterey, CA
93943
Glasgow
Hall 275
(831) 6563048
DSN: 7563048
Fax: (831) 6562595
Email: rdfricke@nps.edu

Professor Ronald D. Fricker, Jr.

Methodological Issues in Biosurveillance
Short Course
12th Biennial CDC & ATSDR Symposium on Statistical Methods
April 6, 2009
Description
This course discusses the strengths and limitations of statistical detection algorithms, with focus on the issues related to
developing, evaluating, and implementing such algorithms in biosurveillance applications. Open challenges to be discussed include:
 Determining the appropriate metric or metrics for assessing and comparing algorithmic performance
 Assessing the utility of comparing algorithmic performance on real versus simulated data
 Modifying existing methods to account for seasonal and other systematic effects in biosurveillance data
 Maximizing algorithmic detection capabilities within tolerable false positive rates
 Understanding when an electronic biosurveillance system “adds value” compared to other methods
Presentation Materials
References
 Bravata, D.M., et al. (2004). Systematic Review: Surveillance Systems for Early Detection of Bioterrorismrelated Diseases,
Annals of Internal Medicine, 140, 910922.
 Brookmeyer, R. and D.F. Stroup (2004). Monitoring the Health of Populations: Statistical Principles & Methods for Public Health Surveillance,
Oxford University Press.
 Buehler, J.W., et al., (2008). Syndromic Surveillance Practice in the United States: Findings from a Survey of
State, Territorial, and Selected Local Health Departments, Advances in Disease Surveillance, 6, 120.
 Chang, J.T., and R.D. Fricker, Jr. (1999). Detecting When a Monotonically Increasing Mean has Crossed a Threshold,
Journal of Quality Technology, 31, 217233.
 Cooper, D.L., et al. (2005). Can Syndromic Surveillance Data Detect Local Outbreaks of Communicable Disease?
A Model Using a Historical Cryptosporidiosis Outbreak, Epidemiology and Infection, 134, 1320.
 Dembek, Z.F., Kortepeter, M.G., and J.A. Pavlin. (2007). Discernment Between Deliberate and Natural Infectious
Disease Outbreaks, Epidemiology and Infection, 135, 353371.
 Forsberg, L., Jeffery, C., Ozonoff, A., and M. Pagano (2006). A Spatiotemporal Analysis of Syndromic Data for Biosurveillance,
Statistical Methods in Counterterrorism: Game Theory, Modeling, Syndromic Surveillance, and Biometric Authentication,
A. Wilson, G. Wilson, and D.H. Olwell, eds., Springer, New York, NY, 173191.
 Fraker, S.E., Woodall, W.H., and S. Mousavi (2008). Performance Metrics for Surveillance Schemes,
Quality Engineering, 20, 451464.
 Fricker, R.D., Jr. (2008). Syndromic Surveillance,
in Encyclopedia of Quantitative Risk Assessment, Melnick, E., and Everitt, B. (eds.), John Wiley & Sons Ltd.,
17431752.
 Fricker, R.D., Jr. (2007). Directionally Sensitive
Multivariate Statistical Process Control Methods with Application to Syndromic Surveillance,
Advances in Disease Surveillance, 3:1.
 Fricker, R.D., Jr., and D. Banschbach, Optimizing Biosurveillance
Systems that Use Thresholdbased Event Detection Methods, in submission.
 Fricker, R.D., Jr., and J.T. Chang, The Repeated Twosample Rank (RTR) Procedure: A Nonparmetric Multivariate Individuals Control Charting Methodology (in draft).
 Fricker, R.D., Jr., and J.T. Chang (2008). A Spatiotemporal Methodology for Realtime Biosurveillance,
Quality Engineering, 20, 465477.
 Fricker, R.D., Jr., Hegler, B.L., and D.A. Dunfee (2008).
Comparing Syndromic Surveillance Detection Methods: EARS' Versus a CUSUMBased Methodology,
Statistics in Medicine, 27, 34073429.
 Fricker, R.D., Jr., Knitt, M.C., and C.X. Hu (2008).
Comparing Directionally Sensitive MCUSUM and MEWMA Procedures with Application to Biosurveillance,
Quality Engineering, 20, 478494.
 Fricker, R.D., Jr., and H.R. Rolka (2006). Protecting
Against Biological Terrorism: Statistical Issues in Electronic Biosurveillance, Chance, 19, 413.
 Frisen, M. and C. Sonesson (2005). Optimal Surveillance, Spatial & Syndromic Surveillance for Public Health,
chapter 3, A.B. Lawson and K. Kleinman, eds., John Wiley & Sons.
 Gisberg, J., et al. (2009). Detecting Influenza Epidemics Using Search Engine Query Data, Nature, 457, 10121014.
 Green, M. (2008). Syndromic Surveillance for Detecting Bioterrorist Events – The Right Answer to the Wrong Question?,
briefing at the Naval War College, September 21, 2008.
 Groenewold, M.R. (2007). Comparison of Two Signal Detection Methods in a CoronerBased System for Near RealTime
Mortality Surveillance, Public Health Reports, 122, 521530.
 Hutwagner, L.C., et al. (2005). A Simulation Model for Assessing Aberration Detection Methods Used in Public Health Surveillance
Systems with Limited Baselines, Statistics in Medicine, 24, 543550.
 Hutwagner, L.C., et al. (2005). Comparing Aberration Detection Methods with Simulated Data, Emerging Infectious Diseases, 11, 314316.
 Joner, M.D., Jr., W.H. Woodall, M.R. Reynolds, Jr., and R.D. Fricker, Jr. (2008).
A OneSided MEWMA Chart for Health Surveillance,
Quality and Reliability Engineering International, 24, 503519.
 Kleinman, K., Lazarus, R., and R. Platt (2004). A Generalized Mixed Model Approach for Detecting Incident Clusters of Disease in Small Areas,
with an Application to Biological Terrorism, American Journal of Epidemiology, 159, 217224.
 Kulldorff, M. (1997). A Spatial Scan Statistic, Communications in Statistics, Theory and Methods,
26, 14811496.
 Kulldorff, M. (2001). Prospective Time Periodic Geographical Disease Surveillance Using a Scan Statistic,
Journal of the Royal Statistical Society, Series A (Statistics in Society), 164, 6172.
 Lawson, A.B. and Kleinman,K. (2005). Spatial & Syndromic Surveillance for Public Health, John Wiley & Sons.
 Le Strat, Y., and F. Carrat (1999). Monitoring Epidemiologic Surveillance Data Using Hidden Markov Models,
Statistics in Medicine, 18, 3463–78.
 Lombardo, J.S. and D.L. Breckeridge (2007). Disease Surveillance: A Public Health Informatics Approach, WileyInterscience.
 Meselson, M., et al. (1994). The Sverdlovsk Anthrax Outbreak of 1979, Science, 266, 12021208.
 Montgomery, D.C. (2009). Introduction to Statistical Quality Control, John Wiley & Sons.
 Nordin, J.D., et al. (2005). Simulated Anthrax Attacks and Syndromic Surveillance, Emerging Infectious Diseases, 11, 13941398.
 Reis, B.Y., Pagano, M., and K.D. Mandl (2003). Using Temporal Context to Improve Biosurveillance,
Proceedings of the National Academy of Sciences of the United States of America, 100, 19611965.
 Rogerson, P.A., and I. Yamada (2004). Monitoring Change in Spatial Patterns of Disease: Comparing Univariate and Multivariate
Cumulative Sum Approaches, Statistics in Medicine, 23, 21952214.
 Rolka, H. (2006). Data Analysis Research Issues and Emerging Public Health Biosurveillance Directions,
Statistical Methods in Counterterrorism: Game Theory, Modeling, Syndromic Surveillance, and Biometric Authentication,
A. Wilson, G. Wilson, and D.H. Olwell, eds., Springer, New York, NY, 101107.
 Rolka, H., Burkom, H., Cooper, G.F., Kulldorff, M., Madigan, D., W. Wong (2007). Issues in Applied Statistics for
Public Health Bioterrorism Surveillance Using Multiple Data Streams: Research Needs, Statistics in Medicine, 26, 18341856.
 Rolka, H., Bracy, D., Russell, C., Fram, D., and R. Ball (2005). Using Simulation to Asses the Sensitivity and Specificity
of a Signal Detection Tool for Multidimensional Public Health Surveillance Data, Statistics in Medicine, 24, 551562.
 Sebastiani, P., Mandl, K.D., Szolovits, P., Kohane, I.S., and M.F. Ramoni (2006). A Bayesian Dynamic Model for Influenza (with discussion),
Statistics in Medicine, 25, 18031825.
 Shmueli, G., ISDS web, accessed at https://wiki.cirg.washington.edu/pub/bin/view/Isds/SurveillanceSystemsInPractice.
 Shmueli, G., and S.E. Fienberg (2006). Current and Potential Statistical Methods for Monitoring Multiple Data Streams for Biosurveillance,
Statistical Methods in Counterterrorism: Game Theory, Modeling, Syndromic Surveillance, and Biometric Authentication,
A. Wilson, G. Wilson, and D.H. Olwell, eds., Springer, New York, NY, 109140.
 Siegrist, D., and J. Pavlin (2004). BioALIRT Biosurveillance Detection Algorithm Evaluation,
Morbidity and Mortality Weekly Report, 53, supplement, 152158.
 Sonesson, C. and D. Bock (2003). A Review and Discussion of Prospective Statistical Surveillance in Public Health,
Journal of the Royal Statistical Society, Series A (Statistics and Society), 166, 521.
 Stoto, M.A., Fricker, R.D., Jr., et al. (2006). Evaluating Statistical Methods for Syndromic
Surveillance, Statistical Methods in Counterterrorism: Game Theory, Modeling, Syndromic Surveillance, and Biometric Authentication,
chapter 9, A. Wilson, G. Wilson, and D. Olwell, eds., Springer.
 Stoto, M.A., Schonlau, M., and L.T. Mariano (2004). Syndromic Surveillance: Is it Worth the Effort?, Chance, 17, 1924.
 The Centers for Disease Control and Prevention (2001). Updated Guidelines for Evaluating Public Health Surveillance Systems,
Morbidity and Mortality Weekly Report, 50(RR13), 135.
 Wong, W., Moore, A., Cooper, G., and M. Wagner (2003). WSARE: What’s Strange About Recent Events?, Journal of Urban Health: Bulletin
of the New York Academy of Medicine, 80 (supplement), 66i75i.
 Woodall, W.H. (2006). The Use of Control Charts in HealthCare and PublicHealth Surveillance, Journal of Quality Technology, 38, 116.
 Woodall, W.H., Grigg, O.A., and H.S. Burkom (2007). Research Issues and Ideas on Healthrelated Surveillance,
draft of paper presented at IXth Workshop on Intelligent Statistical Quality Control held in Bejing, China, September 2008.
 Zhang, J., Tsui, F., Wagner, M., and W. Hogan (2003). Detection of Outbreaks from Time Series Data Using Wavelet Transform,
AMIA Annual Symposium Proceedings, 748752.


