Benchmark data and power calculations for evaluating disease outbreak detection methods.
BACKGROUND
Early detection of disease outbreaks enables public health officials to implement immediate disease control and prevention measures. Computer-based syndromic surveillance systems are being implemented to complement reporting by physicians and other health-care professionals to improve the timeliness of disease-outbreak detection. Space-time disease-surveillance methods have been proposed as a supplement to purely temporal statistical methods for outbreak detection to detect localized outbreaks before they spread to larger regions.


OBJECTIVE
The aims of this study were twofold: 1) to design and make available benchmark data sets for evaluating the statistical power of space-time early detection methods and 2) to evaluate the power of the prospective purely temporal and space-time scan statistics by applying them to the benchmark data sets at different parameter settings.


METHODS
Simulated data sets based on the geography and population of New York City were created, including effects of outbreaks of varying size and location. Data sets with no outbreak effects were also created. Scan statistics were then run on these data sets, and the resulting power performances were analyzed and compared.


RESULTS
The prospective space-time scan statistic performs well for a spectrum of outbreak models. By comparison, the prospective purely temporal scan statistic has higher power for detecting citywide outbreaks but lower power for detecting geographically localized outbreaks.


CONCLUSIONS
The benchmark data sets created for this study can be used successfully for formal statistical power evaluations and comparisons. If an anomaly caused by an outbreak is local, purely temporal surveillance methods might be unable to detect it, in which case space-time methods would be necessary for early detection.
Kulldorff
Martin
M
Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, 133 Brookline Avenue, 6th Floor, Boston, MA 02215, USA. martin_kulldorff@hms.harvard.edu
Zhang
Z
Z
Hartman
J
J
Heffernan
R
R
Huang
L
L
Mostashari
F
F
D019985:Benchmarking; D004196:Disease Outbreaks / Q000517:prevention & control*; D015991:Epidemiologic Measurements; D005069:Evaluation Studies as Topic; D006801:Humans; D008962:Models, Theoretical; D011159:Population Surveillance / Q000379:methods*; D040442:Public Health Informatics / Q000295:instrumentation*; D016008:Statistical Distributions