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Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection

The COVID-19 pandemic has illustrated the importance of infection tracking. The role of asymptomatic, undiagnosed individuals in driving infections within this pandemic has become increasingly evident. Modern phylogenetic tools that take into account asymptomatic or undiagnosed individuals can help...

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Autores principales: Perera, Deshan, Perks, Ben, Potemkin, Michael, Liu, Andy, Gordon, Paul M. K., Gill, M. John, Long, Quan, van Marle, Guido
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8673622/
https://www.ncbi.nlm.nih.gov/pubmed/34910769
http://dx.doi.org/10.1371/journal.pone.0261422
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author Perera, Deshan
Perks, Ben
Potemkin, Michael
Liu, Andy
Gordon, Paul M. K.
Gill, M. John
Long, Quan
van Marle, Guido
author_facet Perera, Deshan
Perks, Ben
Potemkin, Michael
Liu, Andy
Gordon, Paul M. K.
Gill, M. John
Long, Quan
van Marle, Guido
author_sort Perera, Deshan
collection PubMed
description The COVID-19 pandemic has illustrated the importance of infection tracking. The role of asymptomatic, undiagnosed individuals in driving infections within this pandemic has become increasingly evident. Modern phylogenetic tools that take into account asymptomatic or undiagnosed individuals can help guide public health responses. We finetuned established phylogenetic pipelines using published SARS-CoV-2 genomic data to examine reasonable estimate transmission networks with the inference of unsampled infection sources. The system utilised Bayesian phylogenetics and TransPhylo to capture the evolutionary and infection dynamics of SARS-CoV-2. Our analyses gave insight into the transmissions within a population including unsampled sources of infection and the results aligned with epidemiological observations. We were able to observe the effects of preventive measures in Canada’s “Atlantic bubble” and in populations such as New York State. The tools also inferred the cross-species disease transmission of SARS-CoV-2 transmission from humans to lions and tigers in New York City’s Bronx Zoo. These phylogenetic tools offer a powerful approach in response to both the COVID-19 and other emerging infectious disease outbreaks.
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spelling pubmed-86736222021-12-16 Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection Perera, Deshan Perks, Ben Potemkin, Michael Liu, Andy Gordon, Paul M. K. Gill, M. John Long, Quan van Marle, Guido PLoS One Research Article The COVID-19 pandemic has illustrated the importance of infection tracking. The role of asymptomatic, undiagnosed individuals in driving infections within this pandemic has become increasingly evident. Modern phylogenetic tools that take into account asymptomatic or undiagnosed individuals can help guide public health responses. We finetuned established phylogenetic pipelines using published SARS-CoV-2 genomic data to examine reasonable estimate transmission networks with the inference of unsampled infection sources. The system utilised Bayesian phylogenetics and TransPhylo to capture the evolutionary and infection dynamics of SARS-CoV-2. Our analyses gave insight into the transmissions within a population including unsampled sources of infection and the results aligned with epidemiological observations. We were able to observe the effects of preventive measures in Canada’s “Atlantic bubble” and in populations such as New York State. The tools also inferred the cross-species disease transmission of SARS-CoV-2 transmission from humans to lions and tigers in New York City’s Bronx Zoo. These phylogenetic tools offer a powerful approach in response to both the COVID-19 and other emerging infectious disease outbreaks. Public Library of Science 2021-12-15 /pmc/articles/PMC8673622/ /pubmed/34910769 http://dx.doi.org/10.1371/journal.pone.0261422 Text en © 2021 Perera et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Perera, Deshan
Perks, Ben
Potemkin, Michael
Liu, Andy
Gordon, Paul M. K.
Gill, M. John
Long, Quan
van Marle, Guido
Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection
title Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection
title_full Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection
title_fullStr Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection
title_full_unstemmed Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection
title_short Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection
title_sort reconstructing sars-cov-2 infection dynamics through the phylogenetic inference of unsampled sources of infection
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8673622/
https://www.ncbi.nlm.nih.gov/pubmed/34910769
http://dx.doi.org/10.1371/journal.pone.0261422
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