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Related Experiment Videos

Pandemic recovery analysis using the dynamic inoperability input-output model.

Joost R Santos¹, Mark J Orsi, Erik J Bond

¹Department of Engineering Management and Systems Engineering, The George Washington University, Washington, DC 20052, USA. joost@gwu.edu

Risk Analysis : an Official Publication of the Society for Risk Analysis

|December 8, 2009

Summary

This summary is machine-generated.

This study enhances the dynamic inoperability input-output model (DIIM) to better capture pandemic impacts. It introduces workforce disruptions and recovery, improving economic modeling for such crises.

Related Experiment Videos

Area of Science:

Economics
Disaster modeling
Operations research

Background:

Input-output modeling traditionally captures economic interdependencies.
Inoperability input-output models (DIIM) extend this to model disruptions.
Existing DIIM formulations do not fully capture pandemic-specific workforce impacts.

Purpose of the Study:

To enhance the DIIM by incorporating workforce inoperability and recovery.
To develop a modeling framework for pandemic-related economic productivity losses.
To simulate pandemic scenarios using the enhanced DIIM.

Main Methods:

Extension of the dynamic inoperability input-output model (DIIM).
Development of a workforce-explicit modeling framework.
Case study simulation in the Commonwealth of Virginia.

Main Results:

The enhanced DIIM accounts for workforce disruptions and recovery dynamics.
The model provides a more accurate assessment of pandemic-induced economic impacts.
The Virginia case study demonstrates the model's applicability.

Conclusions:

The workforce-explicit DIIM offers improved insights into pandemic economic consequences.
This enhanced model is crucial for disaster preparedness and economic resilience.
Further research can refine the modeling of workforce-related economic disruptions.