IDEAS home Printed from https://ideas.repec.org/p/ems/eureir/99515.html
   My bibliography  Save this paper

The benefits of combining early aspecific vaccination with later specific vaccination

Author

Listed:
  • Westerink-Duijzer, L.E.
  • van Jaarsveld, W.L.
  • Dekker, R.

Abstract

Timing is of crucial importance for successful vaccination. To avoid a large outbreak, vaccines are administered preferably as quickly as possible. However, in the early stages of an outbreak the information on the disease is limited and waiting with the intervention allows to design a more tailored vaccination strategy. In this paper we study the resulting tradeoff between timing of vaccination and the effectiveness of the response. We model disease progression using the seminal SIR model, and consider a decision maker who allocates her budget over two vaccine types: an early aspecific vaccine and a later specific vaccine. We analytically characterize the switching curve separating the parameter space region where the late specific vaccine is preferred from the region where the early aspecific type is preferred. More importantly, we show that the decision maker should not only consider pure strategies, i.e., strategies which spend the entire budget on one of the types. Instead, she should suitably invest in both vaccine types to benefit both from the early response and from the good vaccine. We prove that at the switching curve, such a hybrid strategy is strictly better than either of the pure strategies due to the non-linear dynamics of epidemics. Numerical experiments show that the associated benefit of hybrid strategies over pure strategies in terms of reduction of the number of infections may be more than 50%. Such experiments also substantiate our restriction to two vaccine types.

Suggested Citation

  • Westerink-Duijzer, L.E. & van Jaarsveld, W.L. & Dekker, R., 2017. "The benefits of combining early aspecific vaccination with later specific vaccination," Econometric Institute Research Papers EI2017-03, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • Handle: RePEc:ems:eureir:99515
    as

    Download full text from publisher

    File URL: https://repub.eur.nl/pub/99515/EI2017-03.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Adrian Ramirez-Nafarrate & Joshua D. Lyon & John W. Fowler & Ozgur M. Araz, 2015. "Point-of-Dispensing Location and Capacity Optimization via a Decision Support System," Production and Operations Management, Production and Operations Management Society, vol. 24(8), pages 1311-1328, August.
    5. Chantal Nguyen & Jean M Carlson, 2016. "Optimizing Real-Time Vaccine Allocation in a Stochastic SIR Model," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-27, April.
    6. Steven Riley & Joseph T Wu & Gabriel M Leung, 2007. "Optimizing the Dose of Pre-Pandemic Influenza Vaccines to Reduce the Infection Attack Rate," PLOS Medicine, Public Library of Science, vol. 4(6), pages 1-9, June.
    7. Richard C. Larson, 2007. "Simple Models of Influenza Progression Within a Heterogeneous Population," Operations Research, INFORMS, vol. 55(3), pages 399-412, June.
    8. Rachaniotis, Nikolaos P. & Dasaklis, Tom K. & Pappis, Costas P., 2012. "A deterministic resource scheduling model in epidemic control: A case study," European Journal of Operational Research, Elsevier, vol. 216(1), pages 225-231.
    9. Neil M. Ferguson & Derek A. T. Cummings & Christophe Fraser & James C. Cajka & Philip C. Cooley & Donald S. Burke, 2006. "Strategies for mitigating an influenza pandemic," Nature, Nature, vol. 442(7101), pages 448-452, July.
    10. 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.
    11. Adida, Elodie & Dey, Debabrata & Mamani, Hamed, 2013. "Operational issues and network effects in vaccine markets," European Journal of Operational Research, Elsevier, vol. 231(2), pages 414-427.
    12. Westerink-Duijzer, L.E. & van Jaarsveld, W.L. & Wallinga, J. & Dekker, R., 2016. "The most efficient critical vaccination coverage and its equivalence with maximizing the herd effect," Econometric Institute Research Papers EI2016-06, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    13. Hazhir Rahmandad & John Sterman, 2008. "Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models," Management Science, INFORMS, vol. 54(5), pages 998-1014, May.
    14. Silva, Maria Laura & Perrier, Lionel & Cohen, Jean Marie & Paget, William John & Mosnier, Anne & Späth, Hans Martin, 2015. "A literature review to identify factors that determine policies for influenza vaccination," Health Policy, Elsevier, vol. 119(6), pages 697-708.
    15. Neil M. Ferguson & Derek A.T. Cummings & Simon Cauchemez & Christophe Fraser & Steven Riley & Aronrag Meeyai & Sopon Iamsirithaworn & Donald S. Burke, 2005. "Strategies for containing an emerging influenza pandemic in Southeast Asia," Nature, Nature, vol. 437(7056), pages 209-214, September.
    16. Samii, Amir-Behzad & Pibernik, Richard & Yadav, Prashant & Vereecke, Ann, 2012. "Reservation and allocation policies for influenza vaccines," European Journal of Operational Research, Elsevier, vol. 222(3), pages 495-507.
    17. Westerink-Duijzer, L.E. & van Jaarsveld, W.L. & Wallinga, J. & Dekker, R., 2015. "Dose-optimal vaccine allocation over multiple populations," Econometric Institute Research Papers EI2015-29, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    18. Laura J. Kornish & Ralph L. Keeney, 2008. "Repeated Commit-or-Defer Decisions with a Deadline: The Influenza Vaccine Composition," Operations Research, INFORMS, vol. 56(3), pages 527-541, June.
    19. Lotty E. Duijzer & Willem L. van Jaarsveld & Jacco Wallinga & Rommert Dekker, 2018. "Dose†Optimal Vaccine Allocation over Multiple Populations," Production and Operations Management, Production and Operations Management Society, vol. 27(1), pages 143-159, January.
    20. Eva K. Lee & Fan Yuan & Ferdinand H. Pietz & Bernard A. Benecke & Greg Burel, 2015. "Vaccine Prioritization for Effective Pandemic Response," Interfaces, INFORMS, vol. 45(5), pages 425-443, October.
    21. Eskandarzadeh, Saman & Eshghi, Kourosh & Bahramgiri, Mohsen, 2016. "Risk shaping in production planning problem with pricing under random yield," European Journal of Operational Research, Elsevier, vol. 253(1), pages 108-120.
    22. Maria-Laura Silva & Lionel Perrier & Jean Marie Cohen & William Paget & Anne Mosnier & Hans-Martin Späth, 2015. "Literature review of the decision‐making determinants related to the influenza vaccination policy," Post-Print halshs-01307063, HAL.
    23. Joseph T. Wu & Lawrence M. Wein & Alan S. Perelson, 2005. "Optimization of Influenza Vaccine Selection," Operations Research, INFORMS, vol. 53(3), pages 456-476, June.
    24. Soo-Haeng Cho, 2010. "The Optimal Composition of Influenza Vaccines Subject to Random Production Yields," Manufacturing & Service Operations Management, INFORMS, vol. 12(2), pages 256-277, November.
    25. Yarmand, Hamed & Ivy, Julie S. & Denton, Brian & Lloyd, Alun L., 2014. "Optimal two-phase vaccine allocation to geographically different regions under uncertainty," European Journal of Operational Research, Elsevier, vol. 233(1), pages 208-219.
    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. 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.
    2. Lu, Xuefei & Borgonovo, Emanuele, 2023. "Global sensitivity analysis in epidemiological modeling," European Journal of Operational Research, Elsevier, vol. 304(1), pages 9-24.
    3. Thul, Lawrence & Powell, Warren, 2023. "Stochastic optimization for vaccine and testing kit allocation for the COVID-19 pandemic," European Journal of Operational Research, Elsevier, vol. 304(1), pages 325-338.
    4. Vahdani, Behnam & Mohammadi, Mehrdad & Thevenin, Simon & Gendreau, Michel & Dolgui, Alexandre & Meyer, Patrick, 2023. "Fair-split distribution of multi-dose vaccines with prioritized age groups and dynamic demand: The case study of COVID-19," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1249-1272.
    5. Lin, Qi & Zhao, Qiuhong & Lev, Benjamin, 2020. "Cold chain transportation decision in the vaccine supply chain," European Journal of Operational Research, Elsevier, vol. 283(1), pages 182-195.
    6. Almulhim, Tarifa & Barahona, Igor, 2023. "An extended picture fuzzy multicriteria group decision analysis with different weights: A case study of COVID-19 vaccine allocation," Socio-Economic Planning Sciences, Elsevier, vol. 85(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. 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.
    2. 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).
    3. 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).
    4. 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.
    5. 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).
    6. Westerink-Duijzer, L.E. & Schlicher, L.P.J. & Musegaas, M., 2019. "Fair allocations for cooperation problems in vaccination," Econometric Institute Research Papers EI2019-06, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. 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.
    8. Stephen E. Chick & Sameer Hasija & Javad Nasiry, 2017. "Information Elicitation and Influenza Vaccine Production," Operations Research, INFORMS, vol. 65(1), pages 75-96, February.
    9. 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.
    10. Stephen E. Chick & Sameer Hasija & Javad Nasiry, 2017. "Information Elicitation and Influenza Vaccine Production," Operations Research, INFORMS, vol. 65(1), pages 75-96, February.
    11. Guo, Feiyu & Cao, Erbao, 2021. "Can reference points explain vaccine hesitancy? A new perspective on their formation and updating," Omega, Elsevier, vol. 99(C).
    12. Alexandar Angelus & Özalp Özer, 2022. "On the large‐scale production of a new vaccine," Production and Operations Management, Production and Operations Management Society, vol. 31(7), pages 3043-3060, July.
    13. Ali Ekici & Pınar Keskinocak & Julie L. Swann, 2014. "Modeling Influenza Pandemic and Planning Food Distribution," Manufacturing & Service Operations Management, INFORMS, vol. 16(1), pages 11-27, February.
    14. 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.
    15. Westerink-Duijzer, L.E. & van Jaarsveld, W.L. & Wallinga, J. & Dekker, R., 2015. "Dose-optimal vaccine allocation over multiple populations," Econometric Institute Research Papers EI2015-29, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    16. 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.
    17. Yarmand, Hamed & Ivy, Julie S. & Denton, Brian & Lloyd, Alun L., 2014. "Optimal two-phase vaccine allocation to geographically different regions under uncertainty," European Journal of Operational Research, Elsevier, vol. 233(1), pages 208-219.
    18. Tang, Lianhua & Li, Yantong & Bai, Danyu & Liu, Tao & Coelho, Leandro C., 2022. "Bi-objective optimization for a multi-period COVID-19 vaccination planning problem," Omega, Elsevier, vol. 110(C).
    19. Lu, Xuefei & Borgonovo, Emanuele, 2023. "Global sensitivity analysis in epidemiological modeling," European Journal of Operational Research, Elsevier, vol. 304(1), pages 9-24.
    20. Zhang, Jianghua & Long, Daniel Zhuoyu & Li, Yuchen, 2023. "A reliable emergency logistics network for COVID-19 considering the uncertain time-varying demands," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 172(C).

    More about this item

    Keywords

    optimization; vaccination; mathematical modelling; infectious diseases; SIR model;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:ems:eureir:99515. 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: RePub (email available below). General contact details of provider: https://edirc.repec.org/data/feeurnl.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.