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eLife· 2026Q1

Inferring variant-specific effective reproduction numbers from combined case and sequencing data

Marlin D Figgins, Trevor Bedford

Short summary

A new model jointly estimates variant-specific effective reproduction numbers (R_t) and frequencies using US case and sequence data (Jan 2021-Mar 2022), revealing consistent growth advantages for specific SARS-CoV-2 variants across locations.

AI-generated from the title and abstract; the full text is not read.

Key points

  • Developed a model to jointly estimate variant-specific effective reproduction numbers (R_t) and frequencies.
  • Utilized combined US case and genetic sequence data from January 2021 to March 2022.
  • Inferred structured relationships between R_t across time series to estimate fixed variant-specific growth advantages.
  • Estimated consistent growth advantages for specific SARS-CoV-2 variants across different US locations.

AI-generated from the title and abstract; the full text is not read.

Abstract

Accurately estimating relative transmission rates of SARS-CoV-2 variants remains a scientific and public health priority. Recent studies have used the sample proportions of different variants from genetic sequence data to describe variant frequency dynamics and relative transmission rates, but frequencies alone cannot capture the rich epidemiological behavior of SARS-CoV-2. Here, we extend methods for inferring the effective reproduction number of an epidemic using confirmed case data to jointly estimate variant-specific effective reproduction numbers and frequencies of co-circulating variants using cases and sequences across states in the United States from January 2021 to March 2022. Our method can be used to infer structured relationships between effective reproduction numbers across time series, allowing us to estimate fixed variant-specific growth advantages. We use this model to estimate the effective reproduction number of SARS-CoV-2 variants of concern and variants of interest in the United States, and to estimate consistent growth advantages of particular variants across different locations.

The authors' abstract, as published at the source. eLife, 2026 · DOI ↗

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Field: Modeling and Simulation

Modeling and SimulationMathematics