Open AccessReconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection
Author(s) -
Deshan Perera,
Ben Perks,
Michael Potemkin,
Andy Liu,
Paul Gordon,
M. John Gill,
Quan Long,
Guido van Marle
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0261422
Subject(s) - covid-19 , inference , phylogenetic tree , dynamics (music) , biology , coronavirus infections , betacoronavirus , virology , computational biology , medicine , physics , genetics , computer science , outbreak , infectious disease (medical specialty) , artificial intelligence , pathology , gene , acoustics , disease
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.