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在缓慢和快速流行疫情中选择感染性特征,以及 SARS-CoV-2 变体的出现。

Selection for infectivity profiles in slow and fast epidemics, and the rise of SARS-CoV-2 variants.

机构信息

Centre for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS INSERM, PSL Research University, Paris, France.

Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université de Paris, UMR2000, CNRS, Paris, France.

出版信息

Elife. 2022 May 19;11:e75791. doi: 10.7554/eLife.75791.

DOI:10.7554/eLife.75791
PMID:35587653
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9205634/
Abstract

Evaluating the characteristics of emerging SARS-CoV-2 variants of concern is essential to inform pandemic risk assessment. A variant may grow faster if it produces a larger number of secondary infections ("R advantage") or if the timing of secondary infections (generation time) is better. So far, assessments have largely focused on deriving the R advantage assuming the generation time was unchanged. Yet, knowledge of both is needed to anticipate the impact. Here, we develop an analytical framework to investigate the contribution of the R advantage and generation time to the growth advantage of a variant. It is known that selection on a variant with larger R increases with levels of transmission in the community. We additionally show that variants conferring earlier transmission are more strongly favored when the historical strains have fast epidemic growth, while variants conferring later transmission are more strongly favored when historical strains have slow or negative growth. We develop these conceptual insights into a new statistical framework to infer both the R advantage and generation time of a variant. On simulated data, our framework correctly estimates both parameters when it covers time periods characterized by different epidemiological contexts. Applied to data for the Alpha and Delta variants in England and in Europe, we find that Alpha confers a+54% [95% CI, 45-63%] R advantage compared to previous strains, and Delta +140% [98-182%] compared to Alpha, and mean generation times are similar to historical strains for both variants. This work helps interpret variant frequency dynamics and will strengthen risk assessment for future variants of concern.

摘要

评估新出现的 SARS-CoV-2 关注变种的特征对于告知大流行风险评估至关重要。如果变种产生更多的二次感染(“R 优势”),或者二次感染的时间(世代时间)更好,那么它可能会生长得更快。到目前为止,评估主要集中在假设世代时间不变的情况下推导出 R 优势。然而,需要同时了解这两个因素才能预测其影响。在这里,我们开发了一个分析框架来研究 R 优势和世代时间对变种生长优势的贡献。众所周知,选择具有更大 R 的变体的选择随着社区中传播水平的增加而增加。我们还表明,当历史菌株具有快速流行增长时,传播较早的变种更受青睐,而当历史菌株具有缓慢或负增长时,传播较晚的变种更受青睐。我们将这些概念见解发展为一个新的统计框架,以推断变种的 R 优势和世代时间。在模拟数据中,当我们的框架涵盖具有不同流行病学背景的时间段时,它可以正确地估计这两个参数。将其应用于英格兰和欧洲的 Alpha 和 Delta 变体的数据,我们发现与之前的菌株相比,Alpha 赋予+54%[95%置信区间,45-63%]的 R 优势,而与 Alpha 相比,Delta 赋予+140%[98-182%]的 R 优势,两种变体的平均世代时间与历史菌株相似。这项工作有助于解释变体频率动态,并将为未来关注的变体的风险评估提供支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e51d/9205634/335f3326c008/elife-75791-fig5-figsupp3.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e51d/9205634/335f3326c008/elife-75791-fig5-figsupp3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e51d/9205634/fad6d8151122/elife-75791-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e51d/9205634/999c02276e35/elife-75791-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e51d/9205634/44070bd7fac9/elife-75791-fig2-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e51d/9205634/337fc35e1294/elife-75791-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e51d/9205634/6521772550d9/elife-75791-fig3-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e51d/9205634/5b3ce975e2bb/elife-75791-fig3-figsupp2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e51d/9205634/981eddf3332a/elife-75791-fig3-figsupp3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e51d/9205634/4fc87e29dd30/elife-75791-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e51d/9205634/9ab432e22e62/elife-75791-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e51d/9205634/a6e6d892e473/elife-75791-fig5-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e51d/9205634/1eb4db351d28/elife-75791-fig5-figsupp2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e51d/9205634/335f3326c008/elife-75791-fig5-figsupp3.jpg

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