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HBOK 1-023

Title A Sequential Bayesian Control Model for Influenza-Like Illnesses and Early Detection of Intentional Outbreaks
Author(s) K. D. Zamba, Panagiotis Tsiamyrtzis, and Douglas M. Hawkins
Source Quality Engineering
Topic Data Analysis
Abstract An important public health goal is to rapidly detect either emergence of an influenza epidemic or intentional release of a biological agent with flu-like symptoms. Statistical process control (SPC) methods appear to be especially suitable because of their ability to detect sudden shifts against a background of random variability, but they are deficient in assuming exact knowledge of the background prevalence and in relying on over-simplified models. Bayesian models applied to SPC are better suited for this setting of incomplete information of the parameters describing these data. A control algorithm is provided that is capable of acting on near-real-time data to increase the ability to detect surges in the prevalence of flu-like illnesses. A model is proposed that uses sequential update methods to chart the discrepancy between the observed and predicted incidence of disease.
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Reference Code 1-023

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