Population-wide Anomaly Detection
نویسندگان
چکیده
Early detection of disease outbreaks, particularly an outbreak due to an act of bioterrorism, is a critically important problem due to the potential to reduce both morbidity and mortality. One of the most lethal bioterrorism scenarios is a large-scale release of inhalational anthrax. The Population-wide Anomaly Detection and Assessment (PANDA) algorithm [1] is specifically designed to monitor health-care data for the onset of an outbreak caused by an outdoor, airborne release of inhalational anthrax. At the heart of the PANDA algorithm is a causal Bayesian network which models the effects of the outbreak on a population. The most unique aspect of the PANDA algorithm is an approach we will refer to as population-wide anomaly detection in which each individual in the population is represented as a subnetwork of the overall causal Bayesian network. This paper will describe the benefits of the population-wide approach used by PANDA, which include a coherent way to incorporate background knowledge as well as different types of evidence, the ability to combine multiple data sources indicative of an outbreak, and the capability to identify the evidence that contributes the most to the belief that an anthrax outbreak is occurring.
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