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

  1. François Blanquart  Is a corresponding author
  2. Nathanaël Hozé
  3. Benjamin John Cowling
  4. Florence Débarre
  5. Simon Cauchemez
  1. Collège de France, France
  2. Institut Pasteur, UMR2000, CNRS, France
  3. The University of Hong Kong, China
  4. Sorbonne Université, France
  5. Institut Pasteur, France

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 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 favoured when the historical strains have fast epidemic growth, while variants conferring later transmission are more strongly favoured 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.

Data availability

The codes used for the analyses are available at https://github.com/FrancoisBlanquart/selection_variantThe data used for the analysis is previously available data that is publicly available. A cleaned version of the data used for the analysis is also available herehttps://github.com/FrancoisBlanquart/selection_variant

Article and author information

Author details

  1. François Blanquart

    Centre for Interdisciplinary Research in Biology, Collège de France, Paris, France
    For correspondence
    francois.blanquart@college-de-france.fr
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0591-2466
  2. Nathanaël Hozé

    Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
  3. Benjamin John Cowling

    School of Public Health, The University of Hong Kong, Hong Kong, China
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6297-7154
  4. Florence Débarre

    Institute of Ecology and Environmental Sciences of Paris, Sorbonne Université, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2497-833X
  5. Simon Cauchemez

    Mathematical Modelling of Infectious Disease Unit, Institut Pasteur, Paris, France
    Competing interests
    The authors declare that no competing interests exist.

Funding

Centre National de la Recherche Scientifique (Momentum to FB)

  • François Blanquart

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Ben S Cooper, Mahidol University, Thailand

Version history

  1. Received: November 23, 2021
  2. Preprint posted: December 9, 2021 (view preprint)
  3. Accepted: May 8, 2022
  4. Accepted Manuscript published: May 19, 2022 (version 1)
  5. Version of Record published: June 17, 2022 (version 2)
  6. Version of Record updated: June 28, 2022 (version 3)

Copyright

© 2022, Blanquart et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. François Blanquart
  2. Nathanaël Hozé
  3. Benjamin John Cowling
  4. Florence Débarre
  5. Simon Cauchemez
(2022)
Selection for infectivity profiles in slow and fast epidemics, and the rise of SARS-CoV-2 variants
eLife 11:e75791.
https://doi.org/10.7554/eLife.75791

Share this article

https://doi.org/10.7554/eLife.75791

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