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Using temporal context to improve biosurveillance
Proceedings of the National Academy of Sciences of the United States of America, 100 (4): 1961--1965 (2003)
Аннотация
Current efforts to detect covert bioterrorist attacks from increases in hospital visit rates are
plagued by the unpredictable nature of these rates. Although many current systems evaluate hospital
visit data 1 day at a time, we investigate evaluating multiple days at once to lessen the effects of
this unpredictability and to improve both the timeliness and sensitivity of detection. To test this
approach, we introduce simulated disease outbreaks of varying shapes, magnitudes, and durations into
10 years of historical daily visit data from a major tertiary-care metropolitan teaching hospital. We
then investigate the effectiveness of using multiday temporal filters for detecting these simulated
outbreaks within the noisy environment of the historical visit data. Our results show that compared
with the standard 1-day approach, the multiday detection approach significantly increases detection
sensitivity and decreases latency while maintaining a high specificity. We conclude that current
biosurveillance systems should incorporate a wider temporal context to improve their effectiveness.
Furthermore, for increased robustness and performance, hybrid systems should be developed to
capitalize on the complementary strengths of different types of temporal filters.
