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Natural Hazards

, Volume 77, Issue 2, pp 987–1011 | Cite as

A stochastic recovery model of influenza pandemic effects on interdependent workforce systems

  • Amine El Haimar
  • Joost R. Santos
Original Paper

Abstract

Outbreaks of infectious diseases, such as pandemics, can result in adverse consequences and major economic losses across various economic sectors. Based on findings from the 2009 A H1N1 pandemic in the National Capital Region (NCR), this paper presents a recovery analysis for workforce disruptions using economic input–output modeling. The model formulation takes into consideration the dynamic interdependencies across sectors in an economic system in addition to the inherent characteristics of the economic sectors. From a macroeconomic perspective, the risk of the influenza disaster can be modeled using two risk metrics. First, there is the level of inoperability, which represents the percentage difference between the ideal production level and the degraded production level. Second, the economic loss metric represents the financial value associated with the reduced output. The contribution of this work revolves around the modeling of uncertainties triggered by new perturbations to interdependent economic sectors within an influenza pandemic timeline. We model the level of inoperability of economic sectors throughout their recovery horizon from the initial outbreak of the disaster using a dynamic model. Moreover, we use the level of inoperability values to quantify the cumulative economic losses incurred by the sectors within the recovery horizon. Finally, we revisit the 2009 NCR pandemic scenario to demonstrate the use of uncertainty analysis in modeling the inoperability and economic loss behaviors due to time-varying perturbations and their associated ripple effects to interdependent economic sectors.

Keywords

Pandemic Disaster risk analysis New perturbation Uncertainty modeling 

Notes

Acknowledgments

This work was partially funded by National Science Foundation (Award #1361116) in addition to the Department of Engineering Management and Systems Engineering (EMSE) at George Washington University (GWU). The findings and analysis in this work do not reflect the official positions of NSF and GWU.

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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Department of Engineering Management and Systems EngineeringThe George Washington UniversityWashingtonUSA

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