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

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, as...

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Autores principales: Blanquart, François, Hozé, Nathanaël, Cowling, Benjamin John, Débarre, Florence, Cauchemez, Simon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9205634/
https://www.ncbi.nlm.nih.gov/pubmed/35587653
http://dx.doi.org/10.7554/eLife.75791
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author Blanquart, François
Hozé, Nathanaël
Cowling, Benjamin John
Débarre, Florence
Cauchemez, Simon
author_facet Blanquart, François
Hozé, Nathanaël
Cowling, Benjamin John
Débarre, Florence
Cauchemez, Simon
author_sort Blanquart, François
collection PubMed
description 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 both 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.
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spelling pubmed-92056342022-06-18 Selection for infectivity profiles in slow and fast epidemics, and the rise of SARS-CoV-2 variants Blanquart, François Hozé, Nathanaël Cowling, Benjamin John Débarre, Florence Cauchemez, Simon eLife Epidemiology and Global Health 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 both 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. eLife Sciences Publications, Ltd 2022-05-19 /pmc/articles/PMC9205634/ /pubmed/35587653 http://dx.doi.org/10.7554/eLife.75791 Text en © 2022, Blanquart et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Epidemiology and Global Health
Blanquart, François
Hozé, Nathanaël
Cowling, Benjamin John
Débarre, Florence
Cauchemez, Simon
Selection for infectivity profiles in slow and fast epidemics, and the rise of SARS-CoV-2 variants
title Selection for infectivity profiles in slow and fast epidemics, and the rise of SARS-CoV-2 variants
title_full Selection for infectivity profiles in slow and fast epidemics, and the rise of SARS-CoV-2 variants
title_fullStr Selection for infectivity profiles in slow and fast epidemics, and the rise of SARS-CoV-2 variants
title_full_unstemmed Selection for infectivity profiles in slow and fast epidemics, and the rise of SARS-CoV-2 variants
title_short Selection for infectivity profiles in slow and fast epidemics, and the rise of SARS-CoV-2 variants
title_sort selection for infectivity profiles in slow and fast epidemics, and the rise of sars-cov-2 variants
topic Epidemiology and Global Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9205634/
https://www.ncbi.nlm.nih.gov/pubmed/35587653
http://dx.doi.org/10.7554/eLife.75791
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