Syndromic surveillance needs to be (1) transparent, (2) actionable, and (3) flexible. Traditional frequentist approaches to syndromic surveillance, such as cusum charts and scan statistics, tend to fail on all three criteria. First, the validity of the assumptions is generally difficult to check and the methods are hard to modify; second, the false positive rate makes it impossible to be both sensitive to true signal and resistant to spurious signal; and third, the implementation usually requires significant hand-tinkering to adjust background rates for known seasonal affects and other identifiable influences.
OBJECTIVE
This paper describes a Bayesian approach to syndromic surveillance. The method provides more interpretable inference than traditional frequentist approaches. Bayesian methods avoid many of the problems associated with alpha levels and multiple comparisons, and make better use of prior information. The technique is illustrated on simulated data.