Time-varying optimization of COVID-19 vaccine prioritization in the context of limited vaccination capacity
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-...
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| Published in | Nature communications Vol. 12; no. 1; pp. 4673 - 10 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
03.08.2021
Nature Publishing Group Nature Portfolio |
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| 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.
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. |
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| AbstractList | 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. 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. 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.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. 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. 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. |
| ArticleNumber | 4673 |
| Author | Ajelli, Marco Yu, Hongjie Zhou, Xiao-Hua Cai, Jun Wu, Qianhui Yang, Juan Shi, Huilin Han, Shasha Zheng, Wen Zhang, Juanjuan |
| Author_xml | – sequence: 1 givenname: Shasha orcidid: 0000-0001-7388-8125 surname: Han fullname: Han, Shasha organization: Beijing International Center for Mathematical Research, Peking University, Harvard Medical School, Harvard University – sequence: 2 givenname: Jun orcidid: 0000-0001-9495-1226 surname: Cai fullname: Cai, Jun organization: School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education – sequence: 3 givenname: Juan orcidid: 0000-0001-5028-2227 surname: Yang fullname: Yang, Juan organization: School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University – sequence: 4 givenname: Juanjuan surname: Zhang fullname: Zhang, Juanjuan organization: School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education – sequence: 5 givenname: Qianhui surname: Wu fullname: Wu, Qianhui organization: School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education – sequence: 6 givenname: Wen surname: Zheng fullname: Zheng, Wen organization: School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education – sequence: 7 givenname: Huilin surname: Shi fullname: Shi, Huilin organization: School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education – sequence: 8 givenname: Marco orcidid: 0000-0003-1753-4749 surname: Ajelli fullname: Ajelli, Marco organization: Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Laboratory for the Modelling of Biological and Socio-technical Systems, Northeastern University – sequence: 9 givenname: Xiao-Hua orcidid: 0000-0001-7935-1222 surname: Zhou fullname: Zhou, Xiao-Hua email: azhou@math.pku.edu.cn organization: Beijing International Center for Mathematical Research, Peking University, Department of Biostatistics, School of Public Health, Peking University, National Engineering Laboratory of Big Data Analysis and Applied Technology, Peking University – sequence: 10 givenname: Hongjie orcidid: 0000-0002-6335-5648 surname: Yu fullname: Yu, Hongjie email: yhj@fudan.edu.cn organization: School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Department of Infectious Diseases, Huashan Hospital, Fudan University |
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| Snippet | 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... In the context of limited supply, strategies for optimising allocation of COVID-19 vaccines are needed. Here, the authors explore time-varying strategies that... |
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| SubjectTerms | 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 |
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| Title | Time-varying optimization of COVID-19 vaccine prioritization in the context of limited vaccination capacity |
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