0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessAbstract Evolutionary ecology theory provides an avenue to anticipate the future behavior of SARS-CoV-2. Here we quantify the accelerating evolution of SARS-CoV-2 by tracking the SARS-CoV-2 mutation globally, with a focus on the Receptor Binding Domain (RBD) of the spike protein believed to determine infectivity. We estimate that 384 million people were infected by March 1st, 2021, producing up to 10 21 copies of the virus, with one new RBD variant appearing for every 600,000 human infections, resulting in approximately three new effective RBD variants produced daily. Doubling the number of RBD variants every 71.67 days followed by selection of the most infective variants challenges our defenses and calls for a shift to anticipatory, rather than reactive tactics. One-Sentence Summary Accelerating evolution of SARS-CoV-2 demands formulating universal vaccines and treatments based on big-data simulations of possible new variants.
Carlos M. Duarte, David I. Ketcheson, Victor M. Eguı́luz, Susana Agustı́, Juan Fernández-Gracia, Tahira Jamil, Elisa Laiolo, Takashi Gojobori, Intikhab Álam (2021). Rapid Evolution of SARS-CoV-2 Challenges Human Defenses. , DOI: https://doi.org/10.1101/2021.03.27.437300.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Preprint
Year
2021
Authors
9
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1101/2021.03.27.437300
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access