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


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.


Pandemic Disaster risk analysis New perturbation Uncertainty modeling 



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.


  1. Akhtar R, Santos JR (2013) Risk-based input–output analysis of hurricane impacts on interdependent regional workforce systems. Nat Hazards 65(1):391–405CrossRefGoogle Scholar
  2. Alvarez RM, Brehm J (1995) American ambivalence towards abortion policy: development of a heteroskedastic probit model of competing values. Am J Polit Sci 39(4):1026–1055CrossRefGoogle Scholar
  3. Alvarez RM, Brehm J (1997) Are Americans ambivalent toward racial policies? Am J Polit Sci 41(2):345–374CrossRefGoogle Scholar
  4. Bartlett JG (2006) Planning for avian influenza. Ann Intern Med 154:141–144CrossRefGoogle Scholar
  5. Brehm J, Scott G (1993) Donut shops and speed traps: evaluating models of supervision on police behavior. Am J Polit Sci 37(2):555–581CrossRefGoogle Scholar
  6. Centers for Disease Control and Prevention (CDC) (2010) CDC Estimates of 2009 Influenza cases, hospitalizations and deaths, April–December 12, 2009.˙2009˙h1n1.htm. Accessed 15 Jan 2010
  7. Centers for Disease Control and Prevention (CDC) (2012) Update: influenza activity—United States, 2009–2010 season, Morbidity and Mortality Weekly Report (MMWR), 59(29): 901–908. Accessed 16 Feb 2012
  8. Centers for Disease Control and Prevention (CDC) (2014) National Center for health statistics mortality surveillance data. Accessed 24 Oct 2014
  9. Chao DL, Halloran ME, Obenchain CJ, Longini IM (2010) FluTE, a publicly available stochastic influenza epidemic simulation model. PLoS Comput Biol 6(1):e1000656CrossRefGoogle Scholar
  10. Considine J, Shaban RZ, Patrick J, Holzhauser K, Aitken P, Clark M, Fielding E, FitzGerald G (2011) Pandemic (H1N1) 2009 influenza in Australia: absenteeism and redeployment of emergency medicine and nursing staff. Emerg Med Aust 23:615–623CrossRefGoogle Scholar
  11. Department of Homeland Security, National Infrastructure Protection Plan (NIPP) (2009)
  12. Department of Homeland Security, National Response Framework (NRF) (2008)
  13. Dhankhar P, Zhang X, Meltzer MI, Bridges CB (2006) FluWork-Loss 1.0: a manual to assist state and local public health officials in estimating the impact of an influenza pandemic on work day loss. Centers for Disease Control and Prevention, U.S. Department of Health and Human Services, AtlantaGoogle Scholar
  14. El Haimar A, Santos JR (2013) Modeling uncertainties in workforce disruptions from influenza pandemics using dynamic input–output analysis. Risk Anal. doi: 10.1111/risa.12113 Google Scholar
  15. Ferguson NM, Cummings DAT, Fraser C, Cajka JC, Cooley PC (2006) Strategies for mitigating an influenza pandemic. Nature 442:448–452CrossRefGoogle Scholar
  16. Fraser C et al (2009) Pandemic potential of a strain of influenza A (H1N1): early findings. Science 324(5934):1557–1561CrossRefGoogle Scholar
  17. Gentle JE (1998) Random number generation and Monte Carlo methods. Springer, New YorkCrossRefGoogle Scholar
  18. Germann TC, Kadau K, Longini IM, Macken CA (2006) Mitigation strategies for pandemic influenza in the United States. PNAS 103(15):5935–5940CrossRefGoogle Scholar
  19. Haimes YY, Horowitz BM, Lambert JH, Santos JR, Lian C, Crowther KG (2005) Level of inoperability input–output model for interdependent infrastructure sectors. I: theory and methodology. J Infrastruct Syst 11(2):67–79CrossRefGoogle Scholar
  20. Halder N, Kelso JK, Milne GJ (2010) Analysis of the effectiveness of interventions used during the 2009 A/H1N1 influenza pandemic. BMC Public Health 10(168):1–14Google Scholar
  21. Hawryluck L, Lapinsky SE, Stewart TE (2005) Clinical review: SARS-lessons in disaster management. Crit Care 9:384–389. doi: 10.1186/cc3041 CrossRefGoogle Scholar
  22. Isard W (1960) Methods of regional analysis: an introduction to regional science. MIT Press, Cambridge, p 1960Google Scholar
  23. Jiang P, Haimes YY (2004) Risk management for Leontief based interdependent systems. Risk Anal 24(5):1215–1229CrossRefGoogle Scholar
  24. Jung J, Santos JR, Haimes YY (2009) International trade level of inoperability input–output model (IT-IIM): theory and application. Risk Anal 29(1):137–154CrossRefGoogle Scholar
  25. Kujawski E (2006) Multi-period model for disruptive events in interdependent systems. Syst Eng 9(4):281–295CrossRefGoogle Scholar
  26. Leontief W (1936) Quantitative input and output relations in the economic system of the United States. Rev Econ Stat 18(3):105–125CrossRefGoogle Scholar
  27. Lian C, Haimes YY (2006) Managing the risk of terrorism to interdependent infrastructure systems through the dynamic level of inoperability input–output model. Syst Eng 9(3):241–258CrossRefGoogle Scholar
  28. Mebane WR (2000) Coordination, moderation, and institutional balancing in american presidential and house elections. Am Polit Sci Rev 94(1):37–58CrossRefGoogle Scholar
  29. Miller RE, Blair PD (2009) Input–output analysis: foundations and extensions, 2nd edn. University Press, CambridgeCrossRefGoogle Scholar
  30. Orsi MJ, Santos JR (2010) Probabilistic modeling of workforce-based disruptions and input–output analysis of interdependent ripple effects. Econ Syst Res 22(1):3–18CrossRefGoogle Scholar
  31. Paolino P (1998) Voters’ perceptions of candidate viability: uncertainty and the prospects for momentum. Prepared for presentation at the 1998 Annual Meeting of the Midwest Political Science AssociationGoogle Scholar
  32. President of the United States (2003) Homeland security presidential directive 7: critical infrastructure identification, prioritization, and protection.
  33. President of the United States (2011) Presidential policy directive 8: national preparedness.
  34. Resurreccion JZ, Santos JR (2011) Developing an inventory-based prioritization methodology for assessing level of inoperability and economic loss in interdependent sectors. In: IEEE proceedings of systems and information engineering design symposium, pp 176–181Google Scholar
  35. Ruiz-Juri N, Kockelman KM (2006) Evaluation of the trans-texas corridor proposal: application and enhancements of the random-utility-based multiregional input–output model. J Transp Eng 132(7):531–539CrossRefGoogle Scholar
  36. Santos JR, Orsi MJ, Bond EJ (2009) Pandemic recovery analysis using the dynamic level of inoperability input–output model. Risk Anal 29(12):1743–1758CrossRefGoogle Scholar
  37. Santos JR, May L, El Haimar A (2012) Risk-based input–output analysis of influenza epidemic consequences on interdependent workforce sectors. Risk Anal. doi: 10.1111/risa.12002 Google Scholar
  38. Schanzer DL, Zheng H, Gilmore J (2011) Statistical estimates of absenteeism attributable to seasonal and pandemic influenza from the Canadian Labour force survey. BMC Infect Dis 11(90):1–9Google Scholar
  39. Statistics Canada (2013) Impact of H1N1 and seasonal flu on hours worked. Accessed 5 Jan 2013
  40. The Infrastructure Security Partnership (TISP) (2006) Regional disaster resilience: a guide for developing an action plan, Document #19458. American Society of Civil Engineers, RestonGoogle Scholar
  41. US Department of Commerce (1997) Regional multipliers: a user handbook for the regional input–output modeling system. U.S. Government Printing Office, WashingtonGoogle Scholar
  42. World Health Organization (2003) Influenza.
  43. World Health Organization (2010) H1N1 in post-pandemic period.˙vpc˙20100810/en/index.html. Accessed 5 Oct 2010
  44. Xu W et al (2011) Supply-driven dynamic level of inoperability input–output price model for interdependent infrastructure systems. J Infrastruct Syst 17(4):151–162CrossRefGoogle Scholar

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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