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Selective sweeps in SARS-CoV-2 variant competition

The main mathematical result in this paper is that change of variables in the ordinary differential equation (ODE) for the competition of two infections in a Susceptible–Infected–Removed (SIR) model shows that the fraction of cases due to the new variant satisfies the logistic differential equation,...

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Autores principales: Boyle, Laura, Hletko, Sofia, Huang, Jenny, Lee, June, Pallod, Gaurav, Tung, Hwai-Ray, Durrett, Richard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9704709/
https://www.ncbi.nlm.nih.gov/pubmed/36383746
http://dx.doi.org/10.1073/pnas.2213879119
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author Boyle, Laura
Hletko, Sofia
Huang, Jenny
Lee, June
Pallod, Gaurav
Tung, Hwai-Ray
Durrett, Richard
author_facet Boyle, Laura
Hletko, Sofia
Huang, Jenny
Lee, June
Pallod, Gaurav
Tung, Hwai-Ray
Durrett, Richard
author_sort Boyle, Laura
collection PubMed
description The main mathematical result in this paper is that change of variables in the ordinary differential equation (ODE) for the competition of two infections in a Susceptible–Infected–Removed (SIR) model shows that the fraction of cases due to the new variant satisfies the logistic differential equation, which models selective sweeps. Fitting the logistic to data from the Global Initiative on Sharing All Influenza Data (GISAID) shows that this correctly predicts the rapid turnover from one dominant variant to another. In addition, our fitting gives sensible estimates of the increase in infectivity. These arguments are applicable to any epidemic modeled by SIR equations.
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spelling pubmed-97047092022-11-29 Selective sweeps in SARS-CoV-2 variant competition Boyle, Laura Hletko, Sofia Huang, Jenny Lee, June Pallod, Gaurav Tung, Hwai-Ray Durrett, Richard Proc Natl Acad Sci U S A Physical Sciences The main mathematical result in this paper is that change of variables in the ordinary differential equation (ODE) for the competition of two infections in a Susceptible–Infected–Removed (SIR) model shows that the fraction of cases due to the new variant satisfies the logistic differential equation, which models selective sweeps. Fitting the logistic to data from the Global Initiative on Sharing All Influenza Data (GISAID) shows that this correctly predicts the rapid turnover from one dominant variant to another. In addition, our fitting gives sensible estimates of the increase in infectivity. These arguments are applicable to any epidemic modeled by SIR equations. National Academy of Sciences 2022-11-03 2022-11-22 /pmc/articles/PMC9704709/ /pubmed/36383746 http://dx.doi.org/10.1073/pnas.2213879119 Text en Copyright © 2022 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Physical Sciences
Boyle, Laura
Hletko, Sofia
Huang, Jenny
Lee, June
Pallod, Gaurav
Tung, Hwai-Ray
Durrett, Richard
Selective sweeps in SARS-CoV-2 variant competition
title Selective sweeps in SARS-CoV-2 variant competition
title_full Selective sweeps in SARS-CoV-2 variant competition
title_fullStr Selective sweeps in SARS-CoV-2 variant competition
title_full_unstemmed Selective sweeps in SARS-CoV-2 variant competition
title_short Selective sweeps in SARS-CoV-2 variant competition
title_sort selective sweeps in sars-cov-2 variant competition
topic Physical Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9704709/
https://www.ncbi.nlm.nih.gov/pubmed/36383746
http://dx.doi.org/10.1073/pnas.2213879119
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