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Related Experiment Video

Updated: Oct 18, 2025

An R-Based Landscape Validation of a Competing Risk Model
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Managing Physical and Economic Risk for Systems with Multidirectional Network Interdependencies.

Unal Tatar1, Joost R Santos2, Shital A Thekdi3

  • 1College of Emergency Preparedness, Homeland Security and Cybersecurity, University at Albany-State University of New York, Albany, NY, USA.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|September 28, 2021
PubMed
Summary
This summary is machine-generated.

This study integrates network analysis and economic models to assess infrastructure disruption impacts. It quantizes how transportation network failures affect economic performance over time, aiding risk mitigation strategies.

Keywords:
Critical Infrastructuredisaster risk managementfunctional dependency network analysisinoperability input-output modeltransportation network

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Area of Science:

  • Infrastructure resilience
  • Network science
  • Economic modeling

Background:

  • Critical infrastructure networks exhibit increasing interdependence, amplifying disruption consequences.
  • Existing models often analyze physical or economic impacts separately, lacking integrated assessment.
  • The interaction between physical network performance and economic outcomes during recovery is understudied.

Purpose of the Study:

  • To develop a generalizable framework integrating Functional Dependency Network Analysis (FDNA) and Dynamic Inoperability Input-Output Models (DIIM).
  • To assess the degradation of critical infrastructure functionality and its economic consequences over time following disruptions.
  • To provide insights for network managers and policymakers on infrastructure risk mitigation.

Main Methods:

  • Developed a framework combining FDNA for physical network analysis and DIIM for economic impact assessment.
  • Simulated disruptive scenarios (mild, moderate, severe) on a transportation network in Virginia, USA.
  • Analyzed the temporal spread and economic repercussions of physical network disruptions.

Main Results:

  • The integrated framework quantifies the cascading effects of physical infrastructure disruptions on economic performance.
  • Demonstrated varying economic impacts based on disruption severity and duration for a transportation network.
  • Highlighted the critical role of network interdependencies in economic recovery post-disruption.

Conclusions:

  • The FDNA-DIIM framework offers a robust method for evaluating critical infrastructure resilience.
  • Understanding the interplay between physical functionality and economic performance is crucial for effective risk management.
  • Results inform targeted investments to enhance network functionality and economic stability.