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Time-varying optimization of COVID-19 vaccine prioritization in the context of limited vaccination capacity

Author

Listed:
  • Shasha Han

    (Peking University
    Harvard University)

  • Jun Cai

    (Ministry of Education)

  • Juan Yang

    (Ministry of Education
    Shanghai Institute of Infectious Disease and Biosecurity, Fudan University)

  • Juanjuan Zhang

    (Ministry of Education)

  • Qianhui Wu

    (Ministry of Education)

  • Wen Zheng

    (Ministry of Education)

  • Huilin Shi

    (Ministry of Education)

  • Marco Ajelli

    (Indiana University School of Public Health
    Northeastern University)

  • Xiao-Hua Zhou

    (Peking University
    Peking University
    Peking University)

  • Hongjie Yu

    (Ministry of Education
    Shanghai Institute of Infectious Disease and Biosecurity, Fudan University
    Fudan University)

Abstract

Dynamically adapting the allocation of COVID-19 vaccines to the evolving epidemiological situation could be key to reduce COVID-19 burden. Here we developed a data-driven mechanistic model of SARS-CoV-2 transmission to explore optimal vaccine prioritization strategies in China. We found that a time-varying vaccination program (i.e., allocating vaccines to different target groups as the epidemic evolves) can be highly beneficial as it is capable of simultaneously achieving different objectives (e.g., minimizing the number of deaths and of infections). Our findings suggest that boosting the vaccination capacity up to 2.5 million first doses per day (0.17% rollout speed) or higher could greatly reduce COVID-19 burden, should a new wave start to unfold in China with reproduction number ≤1.5. The highest priority categories are consistent under a broad range of assumptions. Finally, a high vaccination capacity in the early phase of the vaccination campaign is key to achieve large gains of strategic prioritizations.

Suggested Citation

  • Shasha Han & Jun Cai & Juan Yang & Juanjuan Zhang & Qianhui Wu & Wen Zheng & Huilin Shi & Marco Ajelli & Xiao-Hua Zhou & Hongjie Yu, 2021. "Time-varying optimization of COVID-19 vaccine prioritization in the context of limited vaccination capacity," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24872-5
    DOI: 10.1038/s41467-021-24872-5
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    Cited by:

    1. González-Parra, Gilberto & Villanueva-Oller, Javier & Navarro-González, F.J. & Ceberio, Josu & Luebben, Giulia, 2024. "A network-based model to assess vaccination strategies for the COVID-19 pandemic by using Bayesian optimization," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    2. Li, Tingting & Guo, Youming, 2022. "Modeling and optimal control of mutated COVID-19 (Delta strain) with imperfect vaccination," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    3. Margaret L. Lind & Murilo Dorion & Amy J. Houde & Mary Lansing & Sarah Lapidus & Russell Thomas & Inci Yildirim & Saad B. Omer & Wade L. Schulz & Jason R. Andrews & Matt D. T. Hitchings & Byron S. Ken, 2023. "Evidence of leaky protection following COVID-19 vaccination and SARS-CoV-2 infection in an incarcerated population," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. Karakaya, Sırma & Balcik, Burcu, 2024. "Developing a national pandemic vaccination calendar under supply uncertainty," Omega, Elsevier, vol. 124(C).

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