IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v66y2018i1p1-17.html
   My bibliography  Save this article

Robust Postdonation Blood Screening Under Prevalence Rate Uncertainty

Author

Listed:
  • Hadi El-Amine

    (Department of Systems Engineering and Operations Research, George Mason University, Fairfax, Virginia 22030)

  • Ebru K. Bish

    (Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061)

  • Douglas R. Bish

    (Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061)

Abstract

Blood products are essential components of any healthcare system, and their safety, in terms of being free of transfusion-transmittable infections, is crucial. While the Food and Drug Administration (FDA) in the United States requires all blood donations to be tested for certain infection types, it does not dictate which particular tests should be used by blood centers. Multiple FDA-licensed blood screening tests are available for each infection type, and screening tests are imperfectly reliable and have different costs. In addition, infection prevalence rates within the donor population are uncertain for both emerging and established infection types. In this setting, the budget-constrained blood center’s objective is to devise a “robust” postdonation bloodscreening scheme that minimizes the risk of an infectious donation being released into the blood supply. Toward this goal, we study the minimization of the transfusion-transmittable infection risk considering regret- and expectation-based objectives, and we characterize structural properties of their optimal solutions. This allows us to gain insight, derive the price of robustness, and develop efficient algorithms. The proposed robust solution lowers the expected infection risk over various FDA-compliant testing schemes as well as the expectation-based scheme under forecast error. These findings have important public policy implications.

Suggested Citation

  • Hadi El-Amine & Ebru K. Bish & Douglas R. Bish, 2018. "Robust Postdonation Blood Screening Under Prevalence Rate Uncertainty," Operations Research, INFORMS, vol. 66(1), pages 1-17, 1-2.
    • Handle: RePEc:inm:oropre:v:66:y:2018:i:1:p:1-17
    DOI: 10.1287/opre.2017.1658
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/opre.2017.1658
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.2017.1658?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    2. Averbakh, Igor & Lebedev, Vasilij, 2005. "On the complexity of minmax regret linear programming," European Journal of Operational Research, Elsevier, vol. 160(1), pages 227-231, January.
    3. Hamed Poorsepahy-Samian & Reza Kerachian & Mohammad Nikoo, 2012. "Water and Pollution Discharge Permit Allocation to Agricultural Zones: Application of Game Theory and Min-Max Regret Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(14), pages 4241-4257, November.
    4. Georgia Perakis & Guillaume Roels, 2008. "Regret in the Newsvendor Model with Partial Information," Operations Research, INFORMS, vol. 56(1), pages 188-203, February.
    5. Caroline T Korves & Sue J Goldie & Megan B Murray, 2006. "Cost-Effectiveness of Alternative Blood-Screening Strategies for West Nile Virus in the United States," PLOS Medicine, Public Library of Science, vol. 3(2), pages 1-1, January.
    6. Fabio Furini & Manuel Iori & Silvano Martello & Mutsunori Yagiura, 2015. "Heuristic and Exact Algorithms for the Interval Min–Max Regret Knapsack Problem," INFORMS Journal on Computing, INFORMS, vol. 27(2), pages 392-405, May.
    7. Jinfeng Yue & Bintong Chen & Min-Chiang Wang, 2006. "Expected Value of Distribution Information for the Newsvendor Problem," Operations Research, INFORMS, vol. 54(6), pages 1128-1136, December.
    8. Patriksson, Michael, 2008. "A survey on the continuous nonlinear resource allocation problem," European Journal of Operational Research, Elsevier, vol. 185(1), pages 1-46, February.
    9. Xie, Shiguang R. & Bish, Douglas R. & Bish, Ebru K. & Slonim, Anthony D. & Stramer, Susan L., 2012. "Safety and waste considerations in donated blood screening," European Journal of Operational Research, Elsevier, vol. 217(3), pages 619-632.
    10. Alexander Shapiro, 2016. "Rectangular Sets of Probability Measures," Operations Research, INFORMS, vol. 64(2), pages 528-541, April.
    11. Vairaktarakis, George L., 2000. "Robust multi-item newsboy models with a budget constraint," International Journal of Production Economics, Elsevier, vol. 66(3), pages 213-226, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hussein El Hajj & Douglas R. Bish & Ebru K. Bish & Denise M. Kay, 2022. "Novel Pooling Strategies for Genetic Testing, with Application to Newborn Screening," Management Science, INFORMS, vol. 68(11), pages 7994-8014, November.
    2. Mengshi Lu & Zuo‐Jun Max Shen, 2021. "A Review of Robust Operations Management under Model Uncertainty," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1927-1943, June.
    3. Nguyen, Ngoc T. & Bish, Ebru K. & Bish, Douglas R., 2021. "Optimal pooled testing design for prevalence estimation under resource constraints," Omega, Elsevier, vol. 105(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jungho Park & Hadi El-Amine & Nevin Mutlu, 2021. "An Exact Algorithm for Large-Scale Continuous Nonlinear Resource Allocation Problems with Minimax Regret Objectives," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 1213-1228, July.
    2. Mengshi Lu & Zuo‐Jun Max Shen, 2021. "A Review of Robust Operations Management under Model Uncertainty," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1927-1943, June.
    3. Xie, Chen & Wang, Liangquan & Yang, Chaolin, 2021. "Robust inventory management with multiple supply sources," European Journal of Operational Research, Elsevier, vol. 295(2), pages 463-474.
    4. Hrayer Aprahamian & Douglas R. Bish & Ebru K. Bish, 2020. "Optimal Group Testing: Structural Properties and Robust Solutions, with Application to Public Health Screening," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 895-911, October.
    5. Wang, Charles X. & Webster, Scott & Zhang, Sidong, 2014. "Robust price-setting newsvendor model with interval market size and consumer willingness-to-pay," International Journal of Production Economics, Elsevier, vol. 154(C), pages 100-112.
    6. Michael O. Ball & Maurice Queyranne, 2009. "Toward Robust Revenue Management: Competitive Analysis of Online Booking," Operations Research, INFORMS, vol. 57(4), pages 950-963, August.
    7. van Eekelen, Wouter, 2023. "Distributionally robust views on queues and related stochastic models," Other publications TiSEM 9b99fc05-9d68-48eb-ae8c-9, Tilburg University, School of Economics and Management.
    8. Georgia Perakis & Melvyn Sim & Qinshen Tang & Peng Xiong, 2023. "Robust Pricing and Production with Information Partitioning and Adaptation," Management Science, INFORMS, vol. 69(3), pages 1398-1419, March.
    9. Guo, Min & Chen, Yu-wang & Wang, Hongwei & Yang, Jian-Bo & Zhang, Keyong, 2019. "The single-period (newsvendor) problem under interval grade uncertainties," European Journal of Operational Research, Elsevier, vol. 273(1), pages 198-216.
    10. Zhao, Yujie & Zhou, Hong & Leus, Roel, 2022. "Recovery from demand disruption: Two-stage financing strategy for a capital-constrained supply chain under uncertainty," European Journal of Operational Research, Elsevier, vol. 303(2), pages 699-718.
    11. Xin, Linwei & Goldberg, David A., 2021. "Time (in)consistency of multistage distributionally robust inventory models with moment constraints," European Journal of Operational Research, Elsevier, vol. 289(3), pages 1127-1141.
    12. Li, Zhaolin & Fu, Qi (Grace), 2017. "Robust inventory management with stock-out substitution," International Journal of Production Economics, Elsevier, vol. 193(C), pages 813-826.
    13. Ng, Tsan Sheng, 2013. "Robust regret for uncertain linear programs with application to co-production models," European Journal of Operational Research, Elsevier, vol. 227(3), pages 483-493.
    14. Zhi Chen & Weijun Xie, 2021. "Regret in the Newsvendor Model with Demand and Yield Randomness," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 4176-4197, November.
    15. Qiu, Ruozhen & Shang, Jennifer & Huang, Xiaoyuan, 2014. "Robust inventory decision under distribution uncertainty: A CVaR-based optimization approach," International Journal of Production Economics, Elsevier, vol. 153(C), pages 13-23.
    16. Houyuan Jiang & Serguei Netessine & Sergei Savin, 2011. "TECHNICAL NOTE---Robust Newsvendor Competition Under Asymmetric Information," Operations Research, INFORMS, vol. 59(1), pages 254-261, February.
    17. Georgia Perakis & Guillaume Roels, 2008. "Regret in the Newsvendor Model with Partial Information," Operations Research, INFORMS, vol. 56(1), pages 188-203, February.
    18. Serrano, Breno & Minner, Stefan & Schiffer, Maximilian & Vidal, Thibaut, 2024. "Bilevel optimization for feature selection in the data-driven newsvendor problem," European Journal of Operational Research, Elsevier, vol. 315(2), pages 703-714.
    19. Qiu, Ruozhen & Sun, Minghe & Lim, Yun Fong, 2017. "Optimizing (s, S) policies for multi-period inventory models with demand distribution uncertainty: Robust dynamic programing approaches," European Journal of Operational Research, Elsevier, vol. 261(3), pages 880-892.
    20. Andersson, Jonas & Jörnsten, Kurt & Nonås, Sigrid Lise & Sandal, Leif & Ubøe, Jan, 2013. "A maximum entropy approach to the newsvendor problem with partial information," European Journal of Operational Research, Elsevier, vol. 228(1), pages 190-200.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:oropre:v:66:y:2018:i:1:p:1-17. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.