Nakamura Naotoshi, Kobashi Yurie, Kim Kwang Su, Park Hyeongki, Tani Yuta, Shimazu Yuzo, Zhao Tianchen, Nishikawa Yoshitaka, Omata Fumiya, Kawashima Moe, Yoshida Makoto, Abe Toshiki, Saito Yoshika, Senoo Yuki, Nonaka Saori, Takita Morihito, Yamamoto Chika, Kawamura Takeshi, Sugiyama Akira, Nakayama Aya, Kaneko Yudai, Jeong Yong Dam, Tatematsu Daiki, Akao Marwa, Sato Yoshitaka, Iwanami Shoya, Fujita Yasuhisa, Wakui Masatoshi, Aihara Kazuyuki, Kodama Tatsuhiko, Shibuya Kenji, Iwami Shingo, Tsubokura Masaharu
interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan.
Department of Radiation Health Management, Fukushima Medical University School of Medicine, Fukushima, Japan.
PLOS Digit Health. 2024 May 3;3(5):e0000497. doi: 10.1371/journal.pdig.0000497. eCollection 2024 May.
As we learned during the COVID-19 pandemic, vaccines are one of the most important tools in infectious disease control. To date, an unprecedentedly large volume of high-quality data on COVID-19 vaccinations have been accumulated. For preparedness in future pandemics beyond COVID-19, these valuable datasets should be analyzed to best shape an effective vaccination strategy. We are collecting longitudinal data from a community-based cohort in Fukushima, Japan, that consists of 2,407 individuals who underwent serum sampling two or three times after a two-dose vaccination with either BNT162b2 or mRNA-1273. Using the individually reconstructed time courses of the vaccine-elicited antibody response based on mathematical modeling, we first identified basic demographic and health information that contributed to the main features of the antibody dynamics, i.e., the peak, the duration, and the area under the curve. We showed that these three features of antibody dynamics were partially explained by underlying medical conditions, adverse reactions to vaccinations, and medications, consistent with the findings of previous studies. We then applied to these factors a recently proposed computational method to optimally fit an "antibody score", which resulted in an integer-based score that can be used as a basis for identifying individuals with higher or lower antibody titers from basic demographic and health information. The score can be easily calculated by individuals themselves or by medical practitioners. Although the sensitivity of this score is currently not very high, in the future, as more data become available, it has the potential to identify vulnerable populations and encourage them to get booster vaccinations. Our mathematical model can be extended to any kind of vaccination and therefore can form a basis for policy decisions regarding the distribution of booster vaccines to strengthen immunity in future pandemics.
正如我们在新冠疫情期间所了解到的，疫苗是传染病防控中最重要的工具之一。迄今为止，已经积累了数量空前的关于新冠疫苗接种的高质量数据。为了应对新冠疫情之后未来可能出现的大流行，应对这些宝贵的数据集进行分析，以制定出最佳的有效疫苗接种策略。我们正在从日本福岛一个以社区为基础的队列中收集纵向数据，该队列由2407名个体组成，他们在接种两剂BNT162b2或mRNA - 1273疫苗后接受了两到三次血清采样。基于数学模型，利用个体重建的疫苗诱导抗体反应的时间进程，我们首先确定了有助于抗体动力学主要特征（即峰值、持续时间和曲线下面积）的基本人口统计学和健康信息。我们发现，抗体动力学的这三个特征部分可以由潜在的医疗状况、疫苗接种不良反应和药物来解释，这与先前研究的结果一致。然后，我们将一种最近提出的计算方法应用于这些因素，以最佳拟合一个“抗体评分”，该评分是一个基于整数的分数，可以作为从基本人口统计学和健康信息中识别抗体滴度较高或较低个体的依据。这个分数可以由个体自己或医生轻松计算得出。虽然目前这个分数的敏感性不是很高，但未来随着更多数据的获取，它有可能识别出易感人群，并鼓励他们接种加强针。我们的数学模型可以扩展到任何类型的疫苗接种，因此可以为未来大流行中关于加强疫苗分配以增强免疫力的政策决策奠定基础。
