Scan statistics for temporal surveillance for biologic terrorism.
BACKGROUND
Intentional releases of biologic agents are often designed to maximize casualties before diagnostic detection. To provide earlier warning, syndromic surveillance requires statistical methods that are sensitive to an abrupt increase in syndromes or symptoms associated with such an attack.


OBJECTIVE
This study compared two different statistical methods for detecting a relatively abrupt increase in incidence. The methods were based on the number of observations in a moving time window.


METHODS
One class of surveillance techniques generates a signal based on values of the generalized likelihood ratio test (GLRT). This surveillance method is relatively well-known and requires simulation, but it is flexible and, by construction, has the appropriate type I error. An alternative surveillance method generates a signal based on the p-values for the conventional scan statistic. This test does not require simulation, complicated formulas, or use of specialized software, but it is based on approximations and thus can overstate or understate the probability of interest.


RESULTS
This study compared statistical methods by using brucellosis data collected by CDC. The methods provided qualitatively similar results.


CONCLUSIONS
Relatively simple modification of existing software should be considered so that when GLRTs are performed, the appropriate function will be maximized. When a health department has data that indicate an unexpected increase in rates but its staff lack experience with existing software for surveillance based on GLRTs, alternative methods that only require computing Poisson probabilities can be used.
Wallenstein
Sylvan
S
Mount Sinai School of Medicine, Box 1023, 1 Gustave Levy Place, New York, NY 10029, USA. sylvan.wallenstein@mssm.edu
Naus
J
J
D023482:Bioterrorism / Q000517:prevention & control*; D004196:Disease Outbreaks / Q000517:prevention & control*; D015991:Epidemiologic Measurements; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D016012:Poisson Distribution; D011159:Population Surveillance / Q000379:methods*; D014481:United States