建模 COVID-19 疫苗接种策略。
Modelling vaccination strategies for COVID-19.
机构信息
Department of Bioengineering, McGill University, Montreal, Quebec, Canada.
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
出版信息
Nat Rev Immunol. 2022 Mar;22(3):139-141. doi: 10.1038/s41577-022-00687-3.
Despite the rapid development of safe and highly effective vaccines against coronavirus disease 2019 (COVID-19), the strategy for their distribution has been and remains contentious. Mathematical models can be used to guide and inform these strategies; however, uncertainties in critical immunological and evolutionary parameters of SARS-CoV-2 can limit the predictive power of models. Notwithstanding these ongoing uncertainties, we discuss how models have been applied to guide health policy decisions related to vaccination against COVID-19, and how they may be applied in the future in the context of booster doses under different scenarios related to disease-specific factors and global distribution.
尽管针对 2019 年冠状病毒病（COVID-19）的安全高效疫苗发展迅速，但疫苗分发策略一直存在争议。数学模型可用于指导和为这些策略提供信息；然而，SARS-CoV-2 的关键免疫学和进化参数中的不确定性会限制模型的预测能力。尽管存在这些持续的不确定性，我们仍讨论了模型如何被应用于指导与 COVID-19 疫苗接种相关的卫生政策决策，以及它们将来如何在与疾病特定因素和全球分布相关的不同情景下的加强针接种方面得到应用。
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