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The unmitigated profile of COVID-19 infectiousness
Quantifying the temporal dynamics of infectiousness of individuals infected with SARS-CoV-2 is crucial for understanding the spread of COVID-19 and for evaluating the effectiveness of mitigation strategies. Many studies have estimated the infectiousness profile using observed serial intervals. Howev...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9391043/ https://www.ncbi.nlm.nih.gov/pubmed/35913120 http://dx.doi.org/10.7554/eLife.79134 |
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author | Sender, Ron Bar-On, Yinon Park, Sang Woo Noor, Elad Dushoff, Jonathan Milo, Ron |
author_facet | Sender, Ron Bar-On, Yinon Park, Sang Woo Noor, Elad Dushoff, Jonathan Milo, Ron |
author_sort | Sender, Ron |
collection | PubMed |
description | Quantifying the temporal dynamics of infectiousness of individuals infected with SARS-CoV-2 is crucial for understanding the spread of COVID-19 and for evaluating the effectiveness of mitigation strategies. Many studies have estimated the infectiousness profile using observed serial intervals. However, statistical and epidemiological biases could lead to underestimation of the duration of infectiousness. We correct for these biases by curating data from the initial outbreak of the pandemic in China (when mitigation was minimal), and find that the infectiousness profile of the original strain is longer than previously thought. Sensitivity analysis shows our results are robust to model structure, assumed growth rate and potential observational biases. Although unmitigated transmission data is lacking for variants of concern (VOCs), previous analyses suggest that the alpha and delta variants have faster within-host kinetics, which we extrapolate to crude estimates of variant-specific unmitigated generation intervals. Knowing the unmitigated infectiousness profile of infected individuals can inform estimates of the effectiveness of isolation and quarantine measures. The framework presented here can help design better quarantine policies in early stages of future epidemics. |
format | Online Article Text |
id | pubmed-9391043 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-93910432022-08-20 The unmitigated profile of COVID-19 infectiousness Sender, Ron Bar-On, Yinon Park, Sang Woo Noor, Elad Dushoff, Jonathan Milo, Ron eLife Computational and Systems Biology Quantifying the temporal dynamics of infectiousness of individuals infected with SARS-CoV-2 is crucial for understanding the spread of COVID-19 and for evaluating the effectiveness of mitigation strategies. Many studies have estimated the infectiousness profile using observed serial intervals. However, statistical and epidemiological biases could lead to underestimation of the duration of infectiousness. We correct for these biases by curating data from the initial outbreak of the pandemic in China (when mitigation was minimal), and find that the infectiousness profile of the original strain is longer than previously thought. Sensitivity analysis shows our results are robust to model structure, assumed growth rate and potential observational biases. Although unmitigated transmission data is lacking for variants of concern (VOCs), previous analyses suggest that the alpha and delta variants have faster within-host kinetics, which we extrapolate to crude estimates of variant-specific unmitigated generation intervals. Knowing the unmitigated infectiousness profile of infected individuals can inform estimates of the effectiveness of isolation and quarantine measures. The framework presented here can help design better quarantine policies in early stages of future epidemics. eLife Sciences Publications, Ltd 2022-08-01 /pmc/articles/PMC9391043/ /pubmed/35913120 http://dx.doi.org/10.7554/eLife.79134 Text en © 2022, Sender, Bar-On et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Computational and Systems Biology Sender, Ron Bar-On, Yinon Park, Sang Woo Noor, Elad Dushoff, Jonathan Milo, Ron The unmitigated profile of COVID-19 infectiousness |
title | The unmitigated profile of COVID-19 infectiousness |
title_full | The unmitigated profile of COVID-19 infectiousness |
title_fullStr | The unmitigated profile of COVID-19 infectiousness |
title_full_unstemmed | The unmitigated profile of COVID-19 infectiousness |
title_short | The unmitigated profile of COVID-19 infectiousness |
title_sort | unmitigated profile of covid-19 infectiousness |
topic | Computational and Systems Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9391043/ https://www.ncbi.nlm.nih.gov/pubmed/35913120 http://dx.doi.org/10.7554/eLife.79134 |
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