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

• Published in: Nature communications Vol. 12; no. 1; pp. 4673 - 10
• Main Authors: Han, Shasha, Cai, Jun, Yang, Juan, Zhang, Juanjuan, Wu, Qianhui, Zheng, Wen, Shi, Huilin, Ajelli, Marco, Zhou, Xiao-Hua, Yu, Hongjie
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 03.08.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/326/596/4130; 692/308/174; 692/699/255; 692/700/1538; Context; Coronaviruses; COVID-19; COVID-19 vaccines; Disease transmission; Epidemiology; Fatalities; Humanities and Social Sciences; Immunization; multidisciplinary; Optimization; Science; Science (multidisciplinary); Severe acute respiratory syndrome coronavirus 2; Vaccines; China
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-24872-5

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. In the context of limited supply, strategies for optimising allocation of COVID-19 vaccines are needed. Here, the authors explore time-varying strategies that adapt to the epidemiological situation and simultaneously optimise for multiple objectives including reducing numbers of infections, hospitalisations, and deaths.