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

Marlin D Figgins, Trevor Bedford
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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.

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频率动力学预测病毒适应度、抗原关系和流行病增长。
在 COVID-19 大流行期间,SARS-CoV-2 变种在传播性和免疫逃逸增加的推动下引发了大规模的感染浪潮。目前的模型只关注变异体频率的变化,而没有将其与内在传播性和免疫逃逸的基本传播机制联系起来。我们介绍了一种将变异动态与这些机制联系起来的框架,展示了宿主群体的免疫力如何与病毒的传播性和免疫逃逸相互作用,从而决定变异的相对适应性。我们提出了一种选择性压力指标,仅利用基因数据就能提供流行病增长的早期信号,这对目前病例报告不足的情况至关重要。此外,我们还证明了潜伏免疫空间模型与免疫学距离的近似性,为了解人群易感性和免疫逃避提供了线索。这些见解完善了实时预测,为研究病毒遗传学、免疫和流行病增长之间的相互作用奠定了基础。
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