Comment
A Bayesian dynamic model for influenza surveillance by Sebastiani et al., Statistics in Medicine (this issue)
Michael A. Stoto,
Corresponding Author
Michael A. Stoto
Center for Domestic and International Health Security, RAND, 1200 South Hayes Street, Arlington, VA, U.S.A.
Center for Domestic and International Health Security, RAND, 1200 South Hayes Street, Arlington, VA, U.S.A.
Search for more papers by this author
Michael A. Stoto,
Corresponding Author
Michael A. Stoto
Center for Domestic and International Health Security, RAND, 1200 South Hayes Street, Arlington, VA, U.S.A.
Center for Domestic and International Health Security, RAND, 1200 South Hayes Street, Arlington, VA, U.S.A.
Search for more papers by this author
First published: 08 May 2006
No abstract is available for this article.
REFERENCES
- 1 Sebastiani P, Mandl KD, Szolovits P, Kohane IS, Ramoni MF. A Bayesian dynamic model for influenza surveillance. Statistics in Medicine (this issue).
- 2 Stoto MA, Jain A, Diamond A, Davies-Cole JO, Adade A, Washington SC, Kidane G, Glymph C. Time series analysis of the District of Columbia's syndromic surveillance data. MMWR 2005; 54(Suppl.): 202.
- 3 Wong WK, Cooper G, Dash D, Levander J, Dowling J, Hogan W, Wagner M. Use of multiple data streams to conduct Bayesian biologic surveillance. MMWR 2005; 54(Suppl.): 63–69.
- 4 Stoto MA, Fricker RD, Jain AA, Davies-Cole JO, Glymph C, Kidane G, Lum G, Jones L, Yuan C. Evaluating statistical methods for syndromic surveillance. In Statistical Methods in Counter-Terrorism: Game Theory, Modeling, Syndromic Surveillance, and Biometric Authentication, D Olwell, AG Wilson, G Wilson (eds). Springer: New York, 2006, forthcoming.
- 5 Stoto MA, Schonlau M, Mariano LT. Syndromic surveillance: Is it worth the effort? Chance 2004; 17: 19–24.
- 6 Siegrist D, Pavlin J. Bio-ALIRT biosurveillance detection algorithm evaluation. MMWR 2004; 53(Suppl.): 152–157.
