Selection for infectivity profiles in slow and fast epidemics, and the rise of SARS-CoV-2 variants
2022
Blanquart, François | Hozé, Nathanaël | Cowling, Benjamin John | Débarre, Florence | Cauchemez, Simon | Centre interdisciplinaire de recherche en biologie (CIRB) ; Labex MemoLife ; École normale supérieure - Paris (ENS-PSL) ; Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris) ; Université Paris Sciences et Lettres (PSL)-École normale supérieure - Paris (ENS-PSL) ; Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris) ; Université Paris Sciences et Lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS) | Modélisation mathématique des maladies infectieuses - Mathematical modelling of Infectious Diseases ; Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité) | The University of Hong Kong (HKU) | Hong Kong Science and Technology Parks Corporation (HKSTP) | Institut d'écologie et des sciences de l'environnement de Paris (iEES Paris) ; Institut de Recherche pour le Développement (IRD)-Sorbonne Université (SU)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | FB was funded by a Momentum grant from CNRS.
International audience
Mostrar más [+] Menos [-]Inglés. 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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