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Forecasting emergence of COVID-19 variants of concern
We consider whether one can forecast the emergence of variants of concern in the SARS-CoV-2 outbreak and similar pandemics. We explore methods of population genetics and identify key relevant principles in both deterministic and stochastic models of spread of infectious disease. Finally, we demonstr...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8870573/ https://www.ncbi.nlm.nih.gov/pubmed/35202422 http://dx.doi.org/10.1371/journal.pone.0264198 |
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author | Miller, James Kyle Elenberg, Kimberly Dubrawski, Artur |
author_facet | Miller, James Kyle Elenberg, Kimberly Dubrawski, Artur |
author_sort | Miller, James Kyle |
collection | PubMed |
description | We consider whether one can forecast the emergence of variants of concern in the SARS-CoV-2 outbreak and similar pandemics. We explore methods of population genetics and identify key relevant principles in both deterministic and stochastic models of spread of infectious disease. Finally, we demonstrate that fitness variation, defined as a trait for which an increase in its value is associated with an increase in net Darwinian fitness if the value of other traits are held constant, is a strong indicator of imminent transition in the viral population. |
format | Online Article Text |
id | pubmed-8870573 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-88705732022-02-25 Forecasting emergence of COVID-19 variants of concern Miller, James Kyle Elenberg, Kimberly Dubrawski, Artur PLoS One Research Article We consider whether one can forecast the emergence of variants of concern in the SARS-CoV-2 outbreak and similar pandemics. We explore methods of population genetics and identify key relevant principles in both deterministic and stochastic models of spread of infectious disease. Finally, we demonstrate that fitness variation, defined as a trait for which an increase in its value is associated with an increase in net Darwinian fitness if the value of other traits are held constant, is a strong indicator of imminent transition in the viral population. Public Library of Science 2022-02-24 /pmc/articles/PMC8870573/ /pubmed/35202422 http://dx.doi.org/10.1371/journal.pone.0264198 Text en https://creativecommons.org/publicdomain/zero/1.0/This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
spellingShingle | Research Article Miller, James Kyle Elenberg, Kimberly Dubrawski, Artur Forecasting emergence of COVID-19 variants of concern |
title | Forecasting emergence of COVID-19 variants of concern |
title_full | Forecasting emergence of COVID-19 variants of concern |
title_fullStr | Forecasting emergence of COVID-19 variants of concern |
title_full_unstemmed | Forecasting emergence of COVID-19 variants of concern |
title_short | Forecasting emergence of COVID-19 variants of concern |
title_sort | forecasting emergence of covid-19 variants of concern |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8870573/ https://www.ncbi.nlm.nih.gov/pubmed/35202422 http://dx.doi.org/10.1371/journal.pone.0264198 |
work_keys_str_mv | AT millerjameskyle forecastingemergenceofcovid19variantsofconcern AT elenbergkimberly forecastingemergenceofcovid19variantsofconcern AT dubrawskiartur forecastingemergenceofcovid19variantsofconcern |