Selection for infectivity profiles in slow and fast epidemics, and the rise of SARS-CoV-2 variants
Year: 2022
Type: article
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 (transmissibility advantage) or if the timing of secondary infections (generation time) is better. So far, assessments have largely focused on deriving the transmissibility 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 transmissibility advantage and generation time to the growth advantage of a variant. We find that the growth advantage depends on the epidemiological context (level of epidemic control). More specifically, 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 statistical framework to infer both the transmissibility 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%] transmissibility 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 and will strengthen risk assessment for future variants of concern.
Source: eLife
DOI: 10.7554/elife.75791
Institutions: Centre National de la Recherche Scientifique, Inserm, Collège de France, Université Paris Sciences et Lettres, Centre Interdisciplinaire de Recherche en Biologie, Institut Pasteur, Université Paris Cité, Génomique évolutive, modélisation et santé, Laboratory of Data Discovery for Health, University of Hong Kong, Université Paris-Est Créteil, Sorbonne Université, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Institut d'écologie et des sciences de l'environnement de Paris, Institut de Recherche pour le Développement
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,500
OpenAlex ID: W4280610813
