| 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. |
| Access Restrictions | ASQ members and journal subscribers |
| Link for .PDF | http://www.asq.org/quality-engineering/2008/10/statistical-process-control-spc/a-sequential-bayesian-control-model-for-influenza-like-illnesses-and-early-detection-of-intentional-outbreaks.pdf |
| Link for HTML | None |
| Reference Code | 1-023 |
HBOK 1-023
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