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Modeling Uncertainties in Workforce Disruptions from Influenza Pandemics Using Dynamic Input-Output Analysis

Authors

  • Amine El Haimar,

    1. Engineering Management and Systems Engineering, The George Washington University, Washington, DC, USA
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  • Joost R. Santos

    Corresponding author
    1. Engineering Management and Systems Engineering, The George Washington University, Washington, DC, USA
    • Address correspondence to Joost R. Santos, Engineering Management and Systems Engineering, The George Washington University, 1776 G Street NW, Suite 101, Washington, DC 20052, USA; joost@gwu.edu

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Abstract

Influenza pandemic is a serious disaster that can pose significant disruptions to the workforce and associated economic sectors. This article examines the impact of influenza pandemic on workforce availability within an interdependent set of economic sectors. We introduce a simulation model based on the dynamic input-output model to capture the propagation of pandemic consequences through the National Capital Region (NCR). The analysis conducted in this article is based on the 2009 H1N1 pandemic data. Two metrics were used to assess the impacts of the influenza pandemic on the economic sectors: (i) inoperability, which measures the percentage gap between the as-planned output and the actual output of a sector, and (ii) economic loss, which quantifies the associated monetary value of the degraded output. The inoperability and economic loss metrics generate two different rankings of the critical economic sectors. Results show that most of the critical sectors in terms of inoperability are sectors that are related to hospitals and health-care providers. On the other hand, most of the sectors that are critically ranked in terms of economic loss are sectors with significant total production outputs in the NCR such as federal government agencies. Therefore, policy recommendations relating to potential mitigation and recovery strategies should take into account the balance between the inoperability and economic loss metrics.

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