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AuthorsDuarte, Carlos M.
Ketcheson, David I.
Eguíluz, Víctor M
KAUST DepartmentApplied Mathematics and Computational Science Program
Biological and Environmental Science and Engineering (BESE) Division
Computational Bioscience Research Center (CBRC)
Computational Bioscience Research Centre (CBRC), King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Extreme Computing Research Center
Marine Science Program
Numerical Mathematics Group
Red Sea Research Center (RSRC)
Permanent link to this recordhttp://hdl.handle.net/10754/676450
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AbstractThe race between pathogens and their hosts is a major evolutionary driver, where both reshuffle their genomes to overcome and reorganize the defenses for infection, respectively. Evolutionary theory helps formulate predictions on the future evolutionary dynamics of SARS-CoV-2, which can be monitored through unprecedented real-time tracking of SARS-CoV-2 population genomics at the global scale. 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 determining infection success. We estimate that the > 820 million people that had been infected by October 5, 2021, produced up to 1021 copies of the virus, with 12 new effective RBD variants appearing, on average, daily. Doubling of the number of RBD variants every 89 days, followed by selection of the most infective variants challenges our defenses and calls for a shift to anticipatory, rather than reactive tactics involving collaborative global sequencing and vaccination
CitationDuarte, C. M., Ketcheson, D. I., Eguíluz, V. M., Agustí, S., Fernández-Gracia, J., Jamil, T., Laiolo, E., Gojobori, T., & Alam, I. (2022). Rapid evolution of SARS-CoV-2 challenges human defenses. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-10097-z
SponsorsFunded by King Abdullah University of Science and technology through research made available to the Computational BioScience Research Center, CMD and TG. TG. VME and JFG acknowledge funding from “la Caixa” Foundation under the project code SR20-00386 (COVID-SHINE). JFG was supported by Direcció General de Política Universitària i Recerca from the government of the Balearic Islands through the postdoctoral program Vicenç Mut
PublisherSpringer Science and Business Media LLC
CollectionsArticles; Biological and Environmental Science and Engineering (BESE) Division; Red Sea Research Center (RSRC); Bioscience Program; Marine Science Program; Applied Mathematics and Computational Science Program; Extreme Computing Research Center; Computational Bioscience Research Center (CBRC); Numerical Mathematics Group; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Except where otherwise noted, this item's license is described as Archived with thanks to Scientific reports under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0
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