SU‐E‐I‐89: Real‐Time Information and Decision Support for Radiological Emergency Response

Abstract

Purpose: Emergency response and medical preparedness for radiological incidents is one of the critical cornerstones for Homeland Security, along with biological and chemical incidents. The recent Fukushima Daiichi nuclear plant incidents underscore the paramount importance of such preparedness and response capability. Such needs are wide‐spread as many nations employ nuclear plants for energy generation. In this work, we focus on development and deployment of a real‐time simulation and decision support system, RealOpt‐CRC, along with the knowledge data bank that can be used by regional/local radiation/public health administrators to prepare for and deal with radiological emergency situations. Methods: Large‐scale simulator for modeling systems operations and performance, computer graphics for mouse‐click facility design and optimization for resource allocation are designed and implemented into a web‐base secured system. The RealOpt system offers operations capability to i)rapidly setup shelters to house the displaced/at‐risk population, ii)determine optimal resource allocation and operations for rapid screening and decontamination; iii)recommend and facilitate practical steps to minimize exposure risk; iv)perform effective population registry for long‐term health monitoring; and v)service the displaced population on day‐to‐day needs. Results: Comparison of current planning versus plans from our system shows a 5‐fold efficiency improvement. This translates to more people being screened and decontaminated within limited time and resources and thus improve safety and health monitoring for the affected population. Further, workers are more confident and operations are smoother and more organized, thus ensuring public confidence and team‐morale. Conclusions: The system has real‐time computation capability and can be used by emergency management administrators for actual strategic and operational planning and execution; to educate and train current and future personnel on decision making under uncertainties; and to simulate responses to catastrophic events through systematic analysis of numerous scenarios, including worst‐case, to learn of erratic as well as efficient response strategies. Real‐time data‐feeds allow re‐configuration on‐the‐fly as the event unfolds. National Science Foundation