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Geographic prioritization of distributing pandemic influenza vaccines

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  • Ozgur Araz
  • Alison Galvani
  • Lauren Meyers

Abstract

Pandemic influenza is an international public health concern. In light of the persistent threat of H5N1 avian influenza and the recent pandemic of A/H1N1swine influenza outbreak, public health agencies around the globe are continuously revising their preparedness plans. The A/H1N1 pandemic of 2009 demonstrated that influenza activity and severity might vary considerably among age groups and locations, and the distribution of an effective influenza vaccine may be significantly delayed and staggered. Thus, pandemic influenza vaccine distribution policies should be tailored to the demographic and spatial structures of communities. Here, we introduce a bi-criteria decision-making framework for vaccine distribution policies that is based on a geospatial and demographically-structured model of pandemic influenza transmission within and between counties of Arizona in the Unites States. Based on data from the 2009–2010 H1N1 pandemic, the policy predicted to reduce overall attack rate most effectively is prioritizing counties expected to experience the latest epidemic waves (a policy that may be politically untenable). However, when we consider reductions in both the attack rate and the waiting period for those seeking vaccines, the widely adopted pro rata policy (distributing according to population size) is also predicted to be an effective strategy. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Ozgur Araz & Alison Galvani & Lauren Meyers, 2012. "Geographic prioritization of distributing pandemic influenza vaccines," Health Care Management Science, Springer, vol. 15(3), pages 175-187, September.
    • Handle: RePEc:kap:hcarem:v:15:y:2012:i:3:p:175-187
    DOI: 10.1007/s10729-012-9199-6
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    1. Shahparvari, Shahrooz & Hassanizadeh, Behnam & Mohammadi, Alireza & Kiani, Behzad & Lau, Kwok Hung & Chhetri, Prem & Abbasi, Babak, 2022. "A decision support system for prioritised COVID-19 two-dosage vaccination allocation and distribution," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    2. Duijzer, Lotty Evertje & van Jaarsveld, Willem & Dekker, Rommert, 2018. "Literature review: The vaccine supply chain," European Journal of Operational Research, Elsevier, vol. 268(1), pages 174-192.
    3. Laura Matrajt & M Elizabeth Halloran & Ira M Longini Jr, 2013. "Optimal Vaccine Allocation for the Early Mitigation of Pandemic Influenza," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-15, March.
    4. Enayati, Shakiba & Özaltın, Osman Y., 2020. "Optimal influenza vaccine distribution with equity," European Journal of Operational Research, Elsevier, vol. 283(2), pages 714-725.
    5. Sanjay Mehrotra & Hamed Rahimian & Masoud Barah & Fengqiao Luo & Karolina Schantz, 2020. "A model of supply‐chain decisions for resource sharing with an application to ventilator allocation to combat COVID‐19," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(5), pages 303-320, August.
    6. Fadaki, Masih & Abareshi, Ahmad & Far, Shaghayegh Maleki & Lee, Paul Tae-Woo, 2022. "Multi-period vaccine allocation model in a pandemic: A case study of COVID-19 in Australia," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    7. Manupati, Vijaya Kumar & Schoenherr, Tobias & Subramanian, Nachiappan & Ramkumar, M. & Soni, Bhanushree & Panigrahi, Suraj, 2021. "A multi-echelon dynamic cold chain for managing vaccine distribution," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    8. Gilani, Hani & Sahebi, Hadi, 2022. "A data-driven robust optimization model by cutting hyperplanes on vaccine access uncertainty in COVID-19 vaccine supply chain," Omega, Elsevier, vol. 110(C).
    9. Sengul Orgut, Irem & Freeman, Nickolas & Lewis, Dwight & Parton, Jason, 2023. "Equitable and effective vaccine access considering vaccine hesitancy and capacity constraints," Omega, Elsevier, vol. 120(C).

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