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在以前和康复的COVID-19疫苗接种个体之间,随着时间的推移,比较中和抗体活性

Lisette de Pillis¹, Rebecca Caffrey², Ge Chen²

¹Department of Mathematics, Harvey Mudd College, 301 Platt Blvd., Claremont, 91711, CA, USA. depillis@hmc.edu.

Scientific reports

|October 6, 2025

概括

接种COVID-19疫苗的人在中和抗体 (NAb) 活性演变中表现出差异. 与原始组相比,康复过的个体通常保持了较高的NAb水平和较慢的衰变.

科学领域:

免疫学免疫学免疫学
病毒学病毒学
数学生物学数学生物学

背景情况:

从2020年11月到2022年4月,收集了COVID-19疫苗接种者的中和抗体 (NAb) 活性的纵向数据.
了解NAb活动动态对于评估疫苗有效性和免疫持续时间至关重要.

研究的目的:

为了比较之前患有COVID-19 (康复) 和接种疫苗后没有 (天真) 的人之间的NAb活动的演变.
通过使用统计和数学方法,随着时间的推移建模和预测NAb活动.

主要方法:

线性混合效应模型与NAb活动的衰变阶段相适应.
非线性混合效应模型,包括一个普通微分方程系统,被应用于整个NAb进化.
来自非线性模型的参数被用来预测未来的NAb水平.

主要成果:

在康复和原始组之间,NAb活动的演变有显著差异.
与原始组相比,康复组的NAb水平较高,NAb衰变速度较慢.
线性和非线性模型都支持了这些发现.

结论:

在COVID-19疫苗接种后,NAb活动动力学在康复和原始个体之间有所不同.
康复后的组表现出更持久的NAb反应.
数学建模为预测抗体衰减和告知疫苗接种策略提供了宝贵的见解.

关键词:

这就是AditxtScoreTM.在 COVID-19 疫情中,线性混合效应模型的线性混合效应模型.在 MATLAB 中,我们可以使用 MATLAB.数学模型的数学模型.在 Monolix Monolix 中使用.中和抗体的抗体非线性混合效应模型一天一小时的ODE.预测预测预测在这个过程中,R是R.这是SARS-Cov-2病毒.

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