NSF - DMS - ATD (2012-2015)
( Brooklyn College: $278,154)
Sequential quickest detection and identification of
multiple co-dependent epidemic outbreaks
Description
This project is key to the development of next generation
quantitative algorithms for detection of epidemic
outbreaks. The investigators address two focus problems
that arise in epidemic surveillance, namely that of quickest
detection of (a) spatially and (b) pathogen heterogeneous
outbreaks. An early and accurate response is achieved by taking
advantage of the co-dependent nature of the corresponding
syndromic observations and by appropriate modeling of this
dependency. To this end, the investigators develop
innovative online quickest detection and sequential
classification techniques to analyze multiple correlated data
streams undergoing distinct changes. These techniques are
assessed through their ability to optimally issue timely
outbreak alerts with minimal false alarm rates. Moreover, the
investigators address the problem of early detection and
identification of an epidemic outbreak by designing a
simultaneous min-max change-point detection and classification
algorithm of a single data stream with unknown post-disorder
characteristics. In this way, the investigators are able
to also address the problem of model uncertainty and build
robust algorithms. Finally, the investigators combine their
expertise by carrying out a multi-faceted comparison of
alternative formulations (especially Bayesian versus min-max)
for the focus problems, thus creating a model-free
state-of-the-art toolkit targeting highly complex
bio-surveillance data.
People
Co-PI
Michael
Ludkovski
This project is central to the detection and identification of abrupt changes in sequential observations in complex multi-source systems. The detection and identification of abrupt changes arises in many different areas. Examples of these areas are the detection of enemy activity, quality control, the detection of intrusions in computer networks and signal detection from multiple sources such as wireless communications. Although the classical problem of quickest detection has been treated in many forms in the literature dating back to the 1930s, the challenges presented by today's fast-growing technologies cannot be properly addressed by the traditional techniques. We intend to address these challenges using a combination of mathematical tools drawn from probability, modeling, stochastic processes and partial differential equations. This is a comprehensive project whose solution will involve the synergy of a variety of mathematical tools. The research proposed will not only present novel methods of incorporating dependencies across channels, but could also potentially transform the systems used in defense, target detection, wireless communications, portfolio management and intrusion prevention of attacks in networks.