Müller Sebastian A, Paltra Sydney, Rehmann Jakob, Nagel Kai, Conrad Tim O F
Technische Universität Berlin, FG Verkehrssystemplanung und Verkehrstelematik, 10623 Berlin, Germany.
Zuse Institute Berlin, 14195 Berlin, Germany.
iScience. 2023 Aug 8;26(9):107554. doi: 10.1016/j.isci.2023.107554. eCollection 2023 Sep 15.
Measurable levels of immunoglobulin G antibodies develop after infections with and vaccinations against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). These antibody levels are dynamic: due to waning, antibody levels will drop over time. During the COVID-19 pandemic, multiple models predicting infection dynamics were used by policymakers to support the planning of public health policies. Explicitly integrating antibody and waning effects into the models is crucial for reliable calculations of individual infection risk. However, only few approaches have been suggested that explicitly treat these effects. This paper presents a methodology that explicitly models antibody levels and the resulting protection against infection for individuals within an agent-based model. The model was developed in response to the complexity of different immunization sequences and types and is based on neutralization titer studies. This approach allows complex population studies with explicit antibody and waning effects. We demonstrate the usefulness of our model in two use cases.
感染严重急性呼吸综合征冠状病毒2（SARS-CoV-2）以及接种针对该病毒的疫苗后，可检测到免疫球蛋白G抗体水平。这些抗体水平是动态变化的：由于抗体水平会逐渐下降，随着时间推移其水平会降低。在2019冠状病毒病大流行期间，政策制定者使用了多种预测感染动态的模型来支持公共卫生政策的规划。将抗体和抗体衰减效应明确纳入模型对于可靠计算个体感染风险至关重要。然而，仅有少数方法被提出可明确处理这些效应。本文提出了一种方法，该方法在基于主体的模型中明确模拟个体的抗体水平以及由此产生的抗感染保护作用。该模型是针对不同免疫接种顺序和类型的复杂性而开发的，并且基于中和效价研究。这种方法允许进行具有明确抗体和抗体衰减效应的复杂人群研究。我们在两个用例中展示了我们模型的实用性。
