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Abstract:
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A serial signal detection algorithm is developed to monitor irregular series. The algorithm is three-state sequential, and is based on Bayesian thinking. It accounts for non-stationarity, irregularity and seasonality, to capture observations' serial structural details. At stage n, a trichotomous variable governing the states of observations is defined, and a prior distribution for time-indexed serial readings is set. The technicality consists of finding a posterior state probability based on the observed data history, using the posterior as a prior distribution for stage n+1, and sequentially monitoring surges in posterior state probabilities. A sensitivity analysis for validation is conducted and analytical formulas for the predictive distribution are supplied for error management purposes. The method is applied to syndromic surveillance data gathered in the United States (US) District of Columbia metropolitan area.
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