Early detection of SARS-CoV-2 variants with CoVerage platform

Since the emergence of SARS-CoV-2, new virus variants have repeatedly posed challenges for public health systems worldwide. Some of these are designated as Variants of Concern (VOCs) by the World Health Organization (WHO) due to their higher transmissibility, potential to cause more severe disease, or ability to evade immunity from vaccines or prior infections. 

The CoVerage platform for genomic surveillance offers a data-driven approach to identifying potential SARS-CoV-2 Variants of Interest (pVOIs) and VOCs up to three months before official designation. This tool enables researchers, physicians, public health authorities, and vaccine developers to anticipate significant variant trends early. 

Developed by a research group led by Alice McHardy at the German Center for Infection Research (DZIF), CoVerage analyzes SARS-CoV-2 genome sequences from the GISAID database. 

Key features of the method include: 

• Statistical evaluation of amino acid changes in the virus's spike protein 

• Identification of viral strains with significant immune escape potential 

• Use of a matrix inspired by long-term influenza A H3N2 evolution 

• Visualization of variant dynamics through heat maps 

"We have developed a new analysis method for CoVerage that should help make antigenic changes in virus variants more visible," explains Alice McHardy. 

The spike protein is the focus of analysis because of its crucial role in cell entry and its function as a primary vaccine and therapy target. 

To test the method, the team retrospectively analyzed known VOCs such as the Omicron variant. CoVerage flagged them well in advance of WHO classifications. 

"It was interesting to see that the virus variants that were also officially classified as important by the WHO showed significantly higher values in our analyses than other, less noticed variants," explains McHardy. 

The early detection of high-risk variants supports timely vaccine adaptation, enables targeted protective measures, and helps to better safeguard vulnerable populations. 

"Overall, these results underscore the ability of our method to effectively predict the emergence of health-relevant SARS-CoV-2 variants with a growth advantage—well before they reach their maximum frequency or are formally identified by the WHO as concerning," summarizes McHardy. 

MEDICA-tradefair.com; Source: Deutsches Zentrum für Infektionsforschung