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Selection for infectivity profiles in slow and fast epidemics, and the rise of SARS-CoV-2 variants (preprint)
medrxiv; 2021.
Preprint
in English
| medRxiv | ID: ppzbmed-10.1101.2021.12.08.21267454
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.
Full text:
Available
Collection:
Preprints
Database:
medRxiv
Language:
English
Year:
2021
Document Type:
Preprint
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