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Optimizing the Societal Benefits of the Annual Influenza Vaccine: A Stochastic Programming Approach

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  • Osman Y. Özaltın

    (Department of Management Sciences, Faculty of Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada)

  • Oleg A. Prokopyev

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

  • Andrew J. Schaefer

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

  • Mark S. Roberts

    (Department of Health Policy and Management, University of Pittsburgh, Pittsburgh, Pennsylvania 15261)

Abstract

Seasonal influenza is a major public health concern, and the first line of defense is the flu shot. Antigenic drifts and the high rate of influenza transmission require annual updates to the flu shot composition. The World Health Organization recommends which flu strains to include in the annual vaccine, based on surveillance and epidemiological analysis. There are two critical decisions regarding the flu shot design. One is its composition; currently, three strains constitute the flu shot, and they influence vaccine effectiveness. Another critical decision is the timing of the composition decisions, which affects the flu shot production. Both of these decisions have to be made under uncertainty many months before the flu season starts. We quantify the trade-offs involved through a multistage stochastic mixed-integer program that determines the optimal flu shot composition and its timing in a stochastic and dynamic environment.We incorporate risk sensitivity through mean-risk models. Our results provide valuable insights for pressing policy issues.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:oropre:v:59:y:2011:i:5:p:1131-1143
    DOI: 10.1287/opre.1110.0988
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    References listed on IDEAS

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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. Ilke Bakir & Natashia Boland & Brian Dandurand & Alan Erera, 2020. "Sampling Scenario Set Partition Dual Bounds for Multistage Stochastic Programs," INFORMS Journal on Computing, INFORMS, vol. 32(1), pages 145-163, January.
    3. Onur Tavaslıoğlu & Oleg A. Prokopyev & Andrew J. Schaefer, 2019. "Solving Stochastic and Bilevel Mixed-Integer Programs via a Generalized Value Function," Operations Research, INFORMS, vol. 67(6), pages 1659-1677, November.
    4. 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.
    5. 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.
    6. Bakker, Hannah & Dunke, Fabian & Nickel, Stefan, 2020. "A structuring review on multi-stage optimization under uncertainty: Aligning concepts from theory and practice," Omega, Elsevier, vol. 96(C).
    7. Duijzer, Lotty Evertje & van Jaarsveld, Willem & Dekker, Rommert, 2018. "The benefits of combining early aspecific vaccination with later specific vaccination," European Journal of Operational Research, Elsevier, vol. 271(2), pages 606-619.
    8. Ece Zeliha Demirci & Nesim Kohen Erkip, 2020. "Designing intervention scheme for vaccine market: a bilevel programming approach," Flexible Services and Manufacturing Journal, Springer, vol. 32(2), pages 453-485, June.
    9. Stephen E. Chick & Sameer Hasija & Javad Nasiry, 2017. "Information Elicitation and Influenza Vaccine Production," Operations Research, INFORMS, vol. 65(1), pages 75-96, February.
    10. Guo, Feiyu & Cao, Erbao, 2021. "Can reference points explain vaccine hesitancy? A new perspective on their formation and updating," Omega, Elsevier, vol. 99(C).
    11. Hamed Mamani & Stephen E. Chick & David Simchi-Levi, 2013. "A Game-Theoretic Model of International Influenza Vaccination Coordination," Management Science, INFORMS, vol. 59(7), pages 1650-1670, July.
    12. Stephen E. Chick & Sameer Hasija & Javad Nasiry, 2017. "Information Elicitation and Influenza Vaccine Production," Operations Research, INFORMS, vol. 65(1), pages 75-96, February.
    13. Lv, Wei & Li, Hongyi & Tang, Jiafu, 2017. "Bargaining model of labor disputes considering social mediation and bounded rationalityAuthor-Name: Liu, Dehai," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1064-1071.
    14. 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).
    15. Muckstadt, John A. & Klein, Michael G. & Jackson, Peter L. & Gougelet, Robert M. & Hupert, Nathaniel, 2023. "Efficient and effective large-scale vaccine distribution," International Journal of Production Economics, Elsevier, vol. 262(C).
    16. Lauren E. Cipriano & Thomas A. Weber, 2018. "Population-level intervention and information collection in dynamic healthcare policy," Health Care Management Science, Springer, vol. 21(4), pages 604-631, December.

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