Communications of the ACM
Study Suggests Algorithm May Help Optimize Future COVID-19 Vaccine Design
The new algorithm monitors the evolution of SARS-CoV-2 lineages and incorporates information such as spike mutations from variants of concern, SARS-CoV-2 genomes, and clinical samples.
Credit: NIAID
Researchers at the University of Hawaii at Manoa (UH) have developed an adaptive algorithm to help optimize the design of next-generation COVID-19 vaccines.
The algorithm tracks the evolution of SARS-CoV-2 lineages and incorporates information like mutations from variants of concern, SARS-CoV-2 genomes, and clinical samples.
The team gleaned samples five days after two patients tested positive for SARS-CoV-2, and comparison of genomic sequencing to data from both the GenBank and GISAID databases confirmed that the samples were related to the B.1.243 lineage; 49 mutations in the SARS-CoV-2 isolates were associated with this lineage.
The algorithm assigned numerical value to each variant of concern, forecast its likely spread across the population, and estimated each variant's probability of further mutation.
The UH team suggested from these results that the algorithm can function as a baseline for choosing the primary structure of vaccines.
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Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA
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