Sensible Agent Technology Improving Coordination and Communication in Biosurveillance Domains

Abstract

Protecting the population from chemical-biological attacks and outbreaks of infectious disease is a fundamental goal of government entities such as the Center for Disease Control (CDC), as well as state and local health agencies. Early warning is critical for saving lives and implementing an effective response, including characterizing disease sources, preventing proliferation, and treating patients. However, such biosurveillance activities are inherently challenging due to a number of complications: • Coordinating participants and disseminating information: Biosurveillance requires coordination between local, state, and federal authorities. Local entities such as treatment facilities must collect information and disseminate to decision-making entities, constrained by communication costs as well as time. • Determining the relevance and reliability of information: The ability of a treatment facility to offer a diagnosis with high confidence is in part a function of resources and expertise at their disposal. Thus, entities receiving symptom and diagnosis information must determine information relevance qualified by source reliability. • Drawing epidemiological conclusions from symptoms and diagnoses: Significant expertise is required to analyze symptoms and diagnoses to assess public risk. Such assessments consider an entire region, drawing together information from all contributing sources. Often few epidemiologists are responsible for monitoring a large area, resulting in analysis and communication overload. To support epidemiologists and increase the effectiveness of biosurveillance activities, the Laboratory for Intelligent Processes and Systems at the University of Texas at Austin (UT:LIPS) is applying its Sensible Agent (SA) multi-agent system (MAS) technology to the biosurveillance domain. Specific SA features and their applicability to the biosurveillance domain follow: