Selection for infectivity profiles in slow and fast epidemics, and the rise of SARS-CoV-2 variants
- Collège de France, France
- Institut Pasteur, UMR2000, CNRS, France
- The University of Hong Kong, China
- Sorbonne Université, France
- 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
François Blanquart
Benjamin John Cowling
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.
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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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
