Information Technology in Support of Early Detection of Bioterrorist Events
Recent history demonstrates that weapons of mass destruction can be built and deployed by almost any individual or group that has an intent to cause harm or that is looking for media attention for its cause. The arsenal of weapons available to the terrorist includes chemical and biological agents. These weapons, banned from wartime usage, have nevertheless proliferated in third world countries. Information on the development and deployment of these weapons has become widely available on the Internet. Materials to produce some agents are also readily available. Certain biological agents pose a particularly insidious threat in that a clandestine release into a population may not be noticed during the incubation period of the resultant disease. Yet, concerning agents such as anthrax, once the symptoms are manifested it is no longer possible to treat the victim and high mortality is inevitable. Contagious agents like smallpox or the plague pose even greater threats. Such agents require early identification of an infected population in order to treat the victims and contain a potentially devastating epidemic.
The present invention, among other things, presents a solution to the aforementioned problems associated with the prior art.
An object of the invention is early detection of health events, such as bio-terrorist attacks, in populations to enable timely responses that save lives.
Another object is to monitor multiple relevant data sets to detect signals from health events in populations that are undetectable from any single data set.
A further object of the invention is to automate monitoring of relevant data sets related to the health of populations.
The present invention is therefore an automated method and system for detecting health events in populations, such as bio-terrorist attacks, that can operate continuously and with minimal human intervention. An embodiment of the invention includes a method for bio-surveillance detection and alerting that subtracts background noise from relevant data sets using a background estimation algorithm to create residual data. The method also includes modeling the effects of a hypothetical anomalous event on the relevant data sets to create replica data. The residual data is matched with the replica data using a detector to detect a real anomalous event similar to the hypothetical anomalous event. An alert is triggered if a real anomalous event similar to the hypothetical anomalous event is detected.
Additional advantages and features of the invention will become apparent from the description which follows, and may be realized by means of the instrumentalities and methods particularly pointed out in the appended claims.
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