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Living with influenza: Impacts of government imposed and voluntarily selected interventions

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  • Nigmatulina, Karima R.
  • Larson, Richard C.

Abstract

Focusing on mitigation strategies for global pandemic influenza, we use elementary mathematical models to evaluate the implementation and timing of non-pharmaceutical intervention strategies such as travel restrictions, social distancing and improved hygiene. A spreadsheet model of infection spread between several linked heterogeneous communities is based on analytical calculations and Monte Carlo simulations. Since human behavior will likely change during the course of a pandemic, thereby altering the dynamics of the disease, we incorporate a feedback parameter into our model to reflect altered behavior. Our results indicate that while a flu pandemic could be devastating; there are coping methods that when implemented quickly and correctly can significantly mitigate the severity of a global outbreak.

Suggested Citation

  • Nigmatulina, Karima R. & Larson, Richard C., 2009. "Living with influenza: Impacts of government imposed and voluntarily selected interventions," European Journal of Operational Research, Elsevier, vol. 195(2), pages 613-627, June.
    • Handle: RePEc:eee:ejores:v:195:y:2009:i:2:p:613-627
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    References listed on IDEAS

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    5. מחקר - ביטוח לאומי, 2006. "Summary for 2005," Working Papers 29, National Insurance Institute of Israel.
    6. Vittoria Colizza & Alain Barrat & Marc Barthelemy & Alain-Jacques Valleron & Alessandro Vespignani, 2007. "Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions," PLOS Medicine, Public Library of Science, vol. 4(1), pages 1-16, January.
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    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Policy responses > Behavioral

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    5. Savachkin, Alex & Uribe, Andrés, 2012. "Dynamic redistribution of mitigation resources during influenza pandemics," Socio-Economic Planning Sciences, Elsevier, vol. 46(1), pages 33-45.
    6. Lu, Xin & Horn, Abigail L. & Su, Jiahao & Jiang, Jiang, 2019. "A Universal Measure for Network Traceability," Omega, Elsevier, vol. 87(C), pages 191-204.
    7. Teytelman, Anna & Larson, Richard C., 2012. "Modeling influenza progression within a continuous-attribute heterogeneous population," European Journal of Operational Research, Elsevier, vol. 220(1), pages 238-250.
    8. James M. Bloodgood & Jeffrey S. Hornsby & Matthew Rutherford & Richard G. McFarland, 2017. "The role of network density and betweenness centrality in diffusing new venture legitimacy: an epidemiological approach," International Entrepreneurship and Management Journal, Springer, vol. 13(2), pages 525-552, June.
    9. Shui-Lien Chen & Hsiang-Ting Hsu & Richard Chinomona, 2023. "How Tourists’ Perceived Risk Affects Behavioral Intention through Crisis Communication in the Post-COVID-19 Era," Mathematics, MDPI, vol. 11(4), pages 1-18, February.
    10. Choudhury, Nishat Alam & Ramkumar, M. & Schoenherr, Tobias & Singh, Shalabh, 2023. "The role of operations and supply chain management during epidemics and pandemics: Potential and future research opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
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