Emma Goldberg Publishes SARS-CoV-2 Variants Research

Emma Goldberg Publishes SARS-CoV-2 Variants Research

Emma Goldberg Publishes SARS-CoV-2 Variants Research

Emma Goldberg, a Los Alamos National Laboratory and New Mexico Consortium scientist, along with colleagues, have recently published their work, Estimating the strength of selection for new SARS-CoV-2 variants, in Science Communications.

With the COVID-19 pandemic, it is difficult to control the virus as it continues to adapt to human hosts, and new variants emerge. Right now we need more sophisticated methods to determine how transmissible a new variant is and whether it is a global threat or not, and this is what the authors of this publication are looking at.

In this paper, the researchers present two models for quantifying the strength of selection for new and emerging variants of COVID-19 relative to the background of contemporaneous variants.

The researchers used two different approaches. The first is derived from classical population genetic methods that relate the increased transmissibility of a COVID-19 variant to the expected frequency of that variant in the population over time. The second, more detailed method used a stochastic (allowing for uncertainty) epidemiological model to predict both the changes in COVID-19 variant frequencies and deaths over time, accounting for natural and random variation in the virus both between and within countries over time.

This research showed that the pattern of globally emerging COVID-19 variants is driven by large increases in the transmissibility of the virus over time. They also found that early detection of concerning variants is possible even when the global frequency of new variants is as small as 5 percent.

Read the entire Los Alamos Daily Post article: LANL: Estimating Strength Of Selection For COVID Variants


Image at top taken from Fig. 1: Estimates of the selection parameter s for United Kingdom (UK), Netherlands (NL), and Japan (JP) and variants D614G, B.1.1.7, B.1.351, and R.1. See the publication in order to see the entire figure.