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Development and Evaluation of a Data-adaptive Algorithm for Univariate Temporal Biosurveillance Data

Description

Numerous recent papers have evaluated algorithms for biosurveillance anomaly detection. Common essential problems in the disparate, evolving data environment include trends, day-of-week effects, and other systematic behavior. Public health monitors have expressed the need for modifiable case definitions, requiring monitoring of time series that cannot be modeled in advance. Thus, automated algorithm selection is required. Recent research showed superior predictive performance of the H-W forecasting method compared to regression based predictors applied to syndromic data. This effort discusses extension to a practical monitoring tool, including selection from parametric and initialization settings based on limited data history, selection criteria for routine updating, specification of confidence limits, and validation of the resulting algorithm.

 

Objective

The objective is to develop and evaluate an operational alerting algorithm appropriate for the variety of time series behavior observed in biosurveillance data. The Holt-Winters (H-W) implementation of generalized exponential smoothing, comparable to complex regression models in predictive capability and far easier to specify and adapt, is built into a robust detection method.

Submitted by elamb on