Risk-Based Policy Optimization for Critical Infrastructure Resilience against a Pandemic Influenza Outbreak
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 4, Issue 2
Abstract
Decisions regarding infrastructure resilience are made under uncertainty and involve trade-offs among competing objectives. An effective way of understanding how uncertainties propagate and understanding trade-offs among multiple objectives is to use a computer simulation that integrates high-level representations of each infrastructure, their interdependencies, and their reactions to a variety of potential disruptions. To address this need for such a decision support tool, this paper considers a multidisciplinary federation of systems dynamics models for the purpose of simulating the response of critical and interconnected homeland infrastructures to a major disruption. With the addition of disease progression simulation, the models provide a high-level integrated analysis of pandemic influenza outbreak. By use of the models, options for mitigation and prevention such as the use of antivirals, surgical masks, and quarantine policies can be assessed. In this paper, the models are augmented with analytical methods of uncertainty analysis to determine the statistics of model outputs from the statistics of model inputs in order to determine the relative importance of the uncertainties in the model inputs and to identify the worst-case scenarios that have a given probability of occurrence. Techniques of reliability-based optimization are incorporated to find a robust optimal strategy for infrastructure resilience in which preparations are optimized for the worst-case scenario.
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Acknowledgments
This study was partly supported by funds from the National Science Foundation’s IGERT graduate program in Reliability and Risk Engineering and Management at Vanderbilt University (Grant No. 0114329), and the Dwight D. Eisenhower Transportation Fellowship for the first author from the U.S. Department of Transportation. The first author was also funded by a summer internship at the Los Alamos National Laboratory. The support is gratefully acknowledged. The authors also thank David Izraelevitz, Rene LeClaire, and Jeanne Fair, all at the Los Alamos National Laboratory, for their assistance.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 4 • Issue 2 • June 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Feb 13, 2015
Accepted: Jul 19, 2017
Published online: Jan 31, 2018
Published in print: Jun 1, 2018
Discussion open until: Jun 30, 2018
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