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

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  • Amine El Haimar
  • Joost Santos

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. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Amine El Haimar & Joost Santos, 2015. "A stochastic recovery model of influenza pandemic effects on interdependent workforce systems," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(2), pages 987-1011, June.
    • Handle: RePEc:spr:nathaz:v:77:y:2015:i:2:p:987-1011
    DOI: 10.1007/s11069-015-1637-6
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    References listed on IDEAS

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    1. Mark Orsi & Joost Santos, 2010. "Probabilistic Modeling Of Workforce-Based Disruptions And Input-Output Analysis Of Interdependent Ripple Effects," Economic Systems Research, Taylor & Francis Journals, vol. 22(1), pages 3-18.
    2. Neil M. Ferguson & Derek A. T. Cummings & Christophe Fraser & James C. Cajka & Philip C. Cooley & Donald S. Burke, 2006. "Strategies for mitigating an influenza pandemic," Nature, Nature, vol. 442(7101), pages 448-452, July.
    3. Rehman Akhtar & Joost Santos, 2013. "Risk-based input–output analysis of hurricane impacts on interdependent regional workforce systems," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 65(1), pages 391-405, January.
    4. Mebane, Walter R., 2000. "Coordination, Moderation, and Institutional Balancing in American Presidential and House Elections," American Political Science Review, Cambridge University Press, vol. 94(1), pages 37-57, March.
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    Cited by:

    1. Luis Pedauga & Francisco Sáez & Blanca L. Delgado-Márquez, 2022. "Macroeconomic lockdown and SMEs: the impact of the COVID-19 pandemic in Spain," Small Business Economics, Springer, vol. 58(2), pages 665-688, February.
    2. Jian Jin & Haoran Zhou, 2023. "A Demand-Side Inoperability Input–Output Model for Strategic Risk Management: Insight from the COVID-19 Outbreak in Shanghai, China," Sustainability, MDPI, vol. 15(5), pages 1-22, February.
    3. Krista Danielle S. Yu & Kathleen B. Aviso & Joost R. Santos & Raymond R. Tan, 2020. "The Economic Impact of Lockdowns: A Persistent Inoperability Input-Output Approach," Economies, MDPI, vol. 8(4), pages 1-14, December.
    4. Krista Danielle S. Yu & Kathleen B. Aviso & Michael Angelo B. Promentilla & Joost R. Santos & Raymond R. Tan, 2016. "A weighted fuzzy linear programming model in economic input–output analysis: an application to risk management of energy system disruptions," Environment Systems and Decisions, Springer, vol. 36(2), pages 183-195, June.

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