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根据稀疏的血清学数据预测接种疫苗后针对SARS-CoV-2的抗体动力学和保护持续时间。

Predicting antibody kinetics and duration of protection against SARS-CoV-2 following vaccination from sparse serological data.

作者信息

Deichmann Julia, Barda Noam, Canetti Michal, Peretz Yovel, Weiss-Ottolenghi Yael, Lustig Yaniv, Regev-Yochay Gili, Lipsitch Marc

机构信息

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.

Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.

出版信息

PLoS Comput Biol. 2025 Jun 18;21(6):e1013192. doi: 10.1371/journal.pcbi.1013192. eCollection 2025 Jun.

Abstract

Vaccination against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) generates an antibody response that shows large inter-individual variability. This variability complicates the use of antibody levels as a correlate of protection and the evaluation of population- and individual-level infection risk without access to broad serological testing. Here, we applied a mathematical model of antibody kinetics to capture individual anti-SARS-CoV-2 IgG antibody trajectories and to identify factors driving the humoral immune response. Subsequently, we evaluated model predictions and inferred the corresponding duration of protection for new individuals based on a single antibody measurement, assuming sparse access to serological testing. We observe a reduced antibody response in older and in male individuals, and in individuals with autoimmune diseases, diabetes and immunosuppression, using data from a longitudinal cohort study conducted in healthcare workers at Sheba Medical Center, Israel, following primary vaccination with the BNT162b2 COVID-19 vaccine. Our results further suggest that model predictions of an individual's antibody response to vaccination can be used to predict the duration of protection when serological data is limited, highlighting the potential of our approach to estimate infection risk over time on both the population and individual level to support public health decision-making in future pandemics.

摘要

接种严重急性呼吸综合征冠状病毒2(SARS-CoV-2)疫苗会产生个体间差异很大的抗体反应。这种变异性使得在无法进行广泛血清学检测的情况下,将抗体水平作为保护的相关指标以及评估群体和个体层面的感染风险变得复杂。在此,我们应用抗体动力学数学模型来捕捉个体抗SARS-CoV-2 IgG抗体轨迹,并确定驱动体液免疫反应的因素。随后,在假设血清学检测机会有限的情况下,我们基于单次抗体测量评估模型预测,并推断新个体的相应保护持续时间。我们利用以色列谢巴医疗中心医护人员在接种BNT162b2 COVID-19疫苗后进行的纵向队列研究数据,观察到老年个体、男性个体以及患有自身免疫性疾病、糖尿病和免疫抑制的个体抗体反应减弱。我们的结果进一步表明,当血清学数据有限时,个体对疫苗接种的抗体反应的模型预测可用于预测保护持续时间,突出了我们的方法在群体和个体层面随时间估计感染风险以支持未来大流行中公共卫生决策的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc4a/12193770/589016d02051/pcbi.1013192.g001.jpg

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