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Dose†Optimal Vaccine Allocation over Multiple Populations

Author

Listed:
  • Lotty E. Duijzer
  • Willem L. van Jaarsveld
  • Jacco Wallinga
  • Rommert Dekker

Abstract

Vaccination is an effective way to prevent an epidemic. It results in immunity for the vaccinated individuals, but it also reduces the infection pressure for unvaccinated people. Thus people may actually escape infection without being vaccinated: the so†called “herd effect.†We analytically study the relation between the herd effect and the vaccination fraction for the seminal SIR compartmental model, which consists of a set of differential equations describing the time course of an epidemic. We prove that the herd effect is in general convex†concave in the vaccination fraction and give precise conditions on the epidemic for the convex part to arise. We derive the significant consequences of these structural insights for allocating a limited vaccine stockpile to multiple non†interacting populations. We identify for each population a unique vaccination fraction that is most efficient per dose of vaccine: our dose†optimal coverage. We characterize the solution of the vaccine allocation problem and we show the crucial importance of the dose†optimal coverage. A single dose of vaccine may be a drop in the ocean, but multiple doses together can save a population. To benefit from this, policy makers should select a subset of populations to which the vaccines are allocated. Focusing on a limited number of populations can make a significant difference, whereas allocating equally to all populations would be substantially less effective.

Suggested Citation

  • 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.
  • Handle: RePEc:bla:popmgt:v:27:y:2018:i:1:p:143-159
    DOI: 10.1111/poms.12788
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    1. Rezapour, Shabnam & Baghaian, Atefe & Naderi, Nazanin & Sarmiento, Juan P., 2023. "Infection transmission and prevention in metropolises with heterogeneous and dynamic populations," European Journal of Operational Research, Elsevier, vol. 304(1), pages 113-138.
    2. ten Eikelder, S.C.M. & van Amerongen, J.H.M., 2023. "Resource allocation problems with expensive function evaluations," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1170-1185.
    3. 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.
    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. 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).
    6. Zéphirin Nganmeni & Roland Pongou & Bertrand Tchantcho & Jean‐Baptiste Tondji, 2022. "Vaccine and inclusion," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 24(5), pages 1101-1123, October.
      • Zéphirin Nganmeni & Roland Pongou & Bertrand Tchantcho & Jean-Baptiste Tondji, 2022. "Vaccine and Inclusion," Working Papers 2202E Classification-C62,, University of Ottawa, Department of Economics.
      • Zéphirin Nganmeni & Roland Pongou & Bertrand Tchantcho & Jean‐baptiste Tondji, 2022. "Vaccine and inclusion," Post-Print hal-04257703, HAL.
    7. 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).
    8. Kargar, Bahareh & MohajerAnsari, Pedram & Esra Büyüktahtakın, İ. & Jahani, Hamed & Talluri, Sri, 2024. "Data-driven modeling for designing a sustainable and efficient vaccine supply chain: A COVID-19 case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    9. 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.
    10. Erdoğan, Güneş & Yücel, Eda & Kiavash, Parinaz & Salman, F. Sibel, 2024. "Fair and effective vaccine allocation during a pandemic," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    11. Mohammadi, Mehrdad & Dehghan, Milad & Pirayesh, Amir & Dolgui, Alexandre, 2022. "Bi‐objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID‐19 pandemic," Omega, Elsevier, vol. 113(C).
    12. Siqing Shan & Qi Yan & Yigang Wei, 2020. "Infectious or Recovered? Optimizing the Infectious Disease Detection Process for Epidemic Control and Prevention Based on Social Media," IJERPH, MDPI, vol. 17(18), pages 1-25, September.
    13. Kumar Mangla, Sachin & Chauhan, Ankur & Kundu, Tanmoy & Mardani, Abbas, 2023. "Emergency order allocation of e-medical supplies due to the disruptive events of the healthcare crisis," Journal of Business Research, Elsevier, vol. 155(PA).
    14. 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.

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