COVID RADAR: Genetic sequencing predicts the virulence of the next variant

This model responds, 2 years after the start of the pandemic, to the need for forecasts, for decision-makers and health systems, of the transmission of the virus and the evolution of the pandemic, according to the emerging mutations of the SARS- CoV-2. Rather than collecting and analyzing the genetic data of circulating strains in real time, in order to identify new variants and assess their transmission capacity, the team has developed an early warning system which, from the genetic sequencing of a few samples can predict how dangerous a new, emerging variant is likely to be.

A meteorology of virology

The team of Dr. Bahrad A. Sokhansanj, research professor at Drexel’s College of Engineering, speaks of “virological modeling” comparable to meteorological modeling, therefore making it possible to predict epidemic peaks as we predict weather alerts.

The model therefore relies on a targeted analysis of the genetic sequence of the virus’s spike protein, the part of the virus that allows it to evade the immune system and infect healthy cells. It is also the part known to have mutated most frequently throughout the pandemic. This genetic data is combined with the factors such as age, sex and geographic location of COVID patients. In addition, the model was fed with data from the largest GISAID (John Hopkins) database on cases of COVID-19 infection.

Using textual analysis, the program quickly focuses on areas of the genetic sequence that are most likely to be linked to changes in variant severity. The program is therefore not only able to validate the predictions it has already made on the existing variant, but to make projections when it encounters new mutations in the spike protein, on the transmission of the emerging variants.

Faster than all existing scans: “We are thus able to make a prediction about the risk of severe disease associated with an emerging variant, even before the laboratories carry out their in vitro and in vivo experiments, or before a sufficient number of people fall ill to be able to analyze the data. epidemiological”.

What does the model predict for emerging variants? The team made their projections on the sub-variants of Omicron emerging after BA.1 and BA.2. The conclusion is concerning: “Future subvariants of Omicron are more likely to cause more severe disease. Although in the real world, this increased disease severity will be mitigated by natural population protection, linked to previous Omicron variants.” The factor has also been taken into account in the modelling.

“Some analyzes suggest that SARS-CoV-2 has only explored 30-40% of the possible spectrum for mutations in its spike protein. While each mutation can impact key properties of the virus, such as virulence and immune escape, being able to quickly identify these variations and understand what they entail is vital, especially for people vulnerable to the virus. ‘infection “.

“The virus can and has decided to surprise us,

the need to increase our global capacity to sequence variants is immense, sequence analysis of emerging variants must be possible, before these strains become a global problem”.

Leave a Comment

Your email address will not be published.