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

Shasha Han, Jun Cai, Juan Yang, Juanjuan Zhang, Qianhui Wu, Wen Zheng, Huilin Shi, Marco Ajelli, Xiao-Hua Zhou () and Hongjie Yu ()
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Shasha Han: Peking University
Jun Cai: Ministry of Education
Juan Yang: Ministry of Education
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
Xiao-Hua Zhou: Peking University
Hongjie Yu: Ministry of Education

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-24872-5

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