Data pertaining to influenza and influenza-like illnesses (ILI) are being used in the United States and around the globe to assess evidence of influenza activity, whether it is the natural course of the flu or intentional release of a biological agent with flu-like symptoms. These data, used in surveillance, are unstable, serially correlated, and non-stationary. An important goal is to detect, as soon as possible, either emergence of an epidemic or release of biological agent whose symptoms may be initially classified as ILI. Statistical methodologies for analyzing these data are currently short of being able to capture all their important structural details and are generally deficient. Tools from statistical process control (SPC) are, on the face of it, ideally suited for the task since they address a problem of detecting sudden shift against a background of random variability. However, traditional SPC methods are generally deficient in assuming exact knowledge of the background prevalence and in relying on over simplified models. The objective of this article is to use a Bayesian model to find statistical and data-driven evidence for a surveillance problem based on the U.S. Sentinel ILI data. Bayesian statistical methodologies applied to SPC are very well suited for this setting of partial but imperfect information on the parameters describing these data. This article provides a control algorithm capable to act in detect-to-warn fashion on near-real-time data, to increase the ability to detect unusual surges in the prevalence of ILIs. We defined a model that uses sequential update methods to chart the discrepancy between the observed and predicted incidence of ILI.
- Bayesian dynamically updated mixture
- Phase I &II
- Statistical process control (SPC)
- Syndromic surveillance