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频率动态可预测病毒适应性、抗原关系及疫情增长。

Frequency dynamics predict viral fitness, antigenic relationships and epidemic growth.

作者信息

Figgins Marlin D, Bedford Trevor

机构信息

Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.

Department of Applied Mathematics, University of Washington, Seattle, WA, USA.

出版信息

medRxiv. 2025 Jan 23:2024.12.02.24318334. doi: 10.1101/2024.12.02.24318334.

Abstract

During the COVID-19 pandemic, SARS-CoV-2 variants drove large waves of infections, fueled by increased transmissibility and immune escape. Current models focus on changes in variant frequencies without linking them to underlying transmission mechanisms of intrinsic transmissibility and immune escape. We introduce a framework connecting variant dynamics to these mechanisms, showing how host population immunity interacts with viral transmissibility and immune escape to determine relative variant fitness. We advance a selective pressure metric that provides an early signal of epidemic growth using genetic data alone, crucial with current underreporting of cases. Additionally, we show that a latent immunity space model approximates immunological distances, offering insights into population susceptibility and immune evasion. These insights refine real-time forecasting and lay the groundwork for research into the interplay between viral genetics, immunity, and epidemic growth.

摘要

在新冠疫情期间,严重急性呼吸综合征冠状病毒2(SARS-CoV-2)变种引发了大规模感染浪潮,传播性增强和免疫逃逸起到了推动作用。当前模型关注变种频率的变化,却未将其与内在传播性和免疫逃逸的潜在传播机制联系起来。我们引入了一个将变种动态与这些机制相联系的框架,展示了宿主群体免疫力如何与病毒传播性和免疫逃逸相互作用,以确定相对变种适应性。我们提出了一种选择压力指标,仅利用基因数据就能提供疫情增长的早期信号,这在当前病例报告不足的情况下至关重要。此外,我们表明潜在免疫空间模型可近似免疫距离,为了解群体易感性和免疫逃逸提供了见解。这些见解完善了实时预测,并为研究病毒遗传学、免疫力和疫情增长之间的相互作用奠定了基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccf3/11771239/92f10628dec2/nihpp-2024.12.02.24318334v2-f0001.jpg

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