Poehler Elliot, Gibson Liam, Lustig Audrey, Moreland Nicole J, McGregor Reuben, James Alex
School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand.
Te Pūnaha Matatini: the Centre for Complex Systems and Networks, Auckland, New Zealand.
Math Med Biol. 2022 Dec 2;39(4):368-381. doi: 10.1093/imammb/dqac008.
Estimating the longevity of an individual's immune response to the SARS-Cov-2 virus is vital for future planning, particularly of vaccine requirements. Neutralizing antibodies (Nabs) are increasingly being recognized as a correlate of protection and while there are many studies that follow the response of a cohort of people, each study alone is not enough to predict the long-term response. Studies use different assays to measure Nabs, making them hard to combine. We present a modelling method that can combine multiple datasets and can be updated as more detailed data becomes available. Combining data from seven published datasets we predict that the NAb decay has two phases, an initial fast but short-lived decay period followed by a longer term and slower decay period.
评估个体对新冠病毒免疫反应的持续时间对于未来规划至关重要，尤其是在疫苗需求方面。中和抗体（Nabs）越来越被认为是一种保护相关性，虽然有许多研究跟踪一群人的反应，但每项单独的研究都不足以预测长期反应。研究使用不同的检测方法来测量中和抗体，这使得它们难以合并。我们提出了一种建模方法，该方法可以合并多个数据集，并可以在获得更详细的数据时进行更新。结合来自七个已发表数据集的数据，我们预测中和抗体的衰减有两个阶段，一个初始快速但短暂的衰减期，随后是一个长期且较慢的衰减期。
