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Optimal Design of the Seasonal Influenza Vaccine with Manufacturing Autonomy

Author

Listed:
  • Osman Y. Özaltın

    (Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina 27695)

  • Oleg A. Prokopyev

    (Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261)

  • Andrew J. Schaefer

    (Department of Computational and Applied Mathematics, Rice University, Houston, Texas 77005)

Abstract

Influenza (flu) is a serious public health concern. The first line of defense is the flu shot, whose composition is updated annually to adjust for frequent mutations of the circulating viruses. The World Health Organization recommends which strains to include in the flu shot based on global surveillance. Vaccine manufacturers produce trivalent and quadrivalent flu shots. The design of the flu shot, however, affects the manufacturers’ capacity and profit. In return, production decisions of the manufacturers affect the societal vaccination benefit by determining coverage and timely availability. We model this two-level hierarchy using a bilevel multistage stochastic mixed-integer program. Calibrated with publicly available data, our model integrates the flu shot composition and manufacturing in a stochastic and dynamic environment. We derive a branch-and-price algorithm to find the global optimal solution. We also propose an effective heuristic to provide the public health planners with a decision aid tool. Finally, we perform numerical experiments to answer important public health policy questions and to quantify the impact of the proposed modeling extensions. A major conclusion of our work is that the vaccine strain of a category that is not expected to be very prevalent and/or that is unlikely to drift in the upcoming season should be selected as early as possible, especially when the selections for other strain categories have to be postponed to improve the flu shot design.

Suggested Citation

  • Osman Y. Özaltın & Oleg A. Prokopyev & Andrew J. Schaefer, 2018. "Optimal Design of the Seasonal Influenza Vaccine with Manufacturing Autonomy," INFORMS Journal on Computing, INFORMS, vol. 30(2), pages 371-387, May.
  • Handle: RePEc:inm:orijoc:v:30:y:2018:i:2:p:371-387
    DOI: 10.1287/ijoc.2017.0786
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    References listed on IDEAS

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    1. Kenan Arifoglu & Sarang Deo & Seyed M. R. Iravani, 2012. "Consumption Externality and Yield Uncertainty in the Influenza Vaccine Supply Chain: Interventions in Demand and Supply Sides," Management Science, INFORMS, vol. 58(6), pages 1072-1091, June.
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    6. Osman Y. Özaltın & Oleg A. Prokopyev & Andrew J. Schaefer & Mark S. Roberts, 2011. "Optimizing the Societal Benefits of the Annual Influenza Vaccine: A Stochastic Programming Approach," Operations Research, INFORMS, vol. 59(5), pages 1131-1143, October.
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    Cited by:

    1. Lin, Qi & Zhao, Qiuhong & Lev, Benjamin, 2022. "Influenza vaccine supply chain coordination under uncertain supply and demand," European Journal of Operational Research, Elsevier, vol. 297(3), pages 930-948.
    2. Guo, Feiyu & Cao, Erbao, 2021. "Can reference points explain vaccine hesitancy? A new perspective on their formation and updating," Omega, Elsevier, vol. 99(C).
    3. Enayati, Shakiba & Özaltın, Osman Y., 2020. "Optimal influenza vaccine distribution with equity," European Journal of Operational Research, Elsevier, vol. 283(2), pages 714-725.
    4. Rahman Khorramfar & Osman Y. Özaltın & Karl G. Kempf & Reha Uzsoy, 2022. "Managing Product Transitions: A Bilevel Programming Approach," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2828-2844, September.
    5. Rahman Khorramfar & Osman Ozaltin & Reha Uzsoy & Karl Kempf, 2024. "Coordinating Resource Allocation during Product Transitions Using a Multifollower Bilevel Programming Model," Papers 2401.17402, arXiv.org.
    6. Roy Lothan & Noa Gutman & Dan Yamin, 2022. "Country versus pharmaceutical company interests for hepatitis C treatment," Health Care Management Science, Springer, vol. 25(4), pages 725-749, December.

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