A Stochastic Model for Evaluating Interconnected Critical Infrastructure Decontamination and Recovery
Citation:
Richter, B., T. Joshi, R. James, T. Boe, W. Calfee, L. Mickelsen, P. Lemieux, AND Joe Wood. A Stochastic Model for Evaluating Interconnected Critical Infrastructure Decontamination and Recovery. EPA Decon Conference, Norfolk,VA, November 19 - 21, 2019.
Impact/Purpose:
This presentation is for the 2019 EPA International Decontamination Research and Development Conference on November 19-21, 2019 at the Sheraton Norfolk Waterside Hotel, Norfolk, VA. This provides an opportunity to analyze the allocation of government resources and assess response strategies in the restoration effort. The stochastic nature of this algorithm enables the model to account for discrete events and capture the inherent uncertainty in the infrastructure restoration process. In this study, the model is applied to notional biological contamination scenarios, where the direct impact on critical infrastructure assets is assessed using data from FEMA’s HAZUS database overlaid on a GIS map of a metropolitan area.
Description:
Large-scale chemical, biological, radiological, and nuclear (CBRN) incidents have the potential to impact a wide area that would include core infrastructure assets. In such an event, the impact may extend beyond directly affected infrastructure sectors due to the interconnected nature of critical infrastructure systems. The process of infrastructure restoration is modeled as reactions, where resources from one or more infrastructure sectors are used to restore service in another sector. The model also accounts for a decrease in productivity due to injuries and/or temporary relocation of the population as a result of the CBRN event and an increase in productivity due to input from external forces (e.g., government involvement) to repair damaged critical infrastructure.