Phylogenetic Analysis to Detect COVID Superspreaders

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Author(s)Jungck, John R.
Author(s)Ko, Hajae
Date Accessioned2024-02-05T21:00:03Z
Date Available2024-02-05T21:00:03Z
Publication Date2023-10-12
DescriptionThis article was originally published in Microbiology Research Journal International. The version of record is available at: https://doi.org/10.9734/mrji/2023/v33i81400. © 2023 Jungck and Ko.
AbstractAims: Detection of superspreading events by phylogenetic analysis of nucleotide sequences from a population of individuals collected from a narrow time interval. Study Design: Retrieve nucleic acid sequences, construct multiple sequence alignments, and build phylogenetic networks to determine sources of infection. Place and Duration of Study: This study was performed at the Delaware Biotechnology Institute of the University of Delaware over the period: June-August, 2022. The data used were from the GIS AID database. Methodology: Sequences for analysis were sampled from the GISAID initiative’s open-access SARS-CoV-2 genome database. We selected high-quality nucleotide sequences submitted by Delaware labs between March 18 and April 14, 2021, an important period of 4 weeks which saw the Alpha variant spread rapidly in the Delaware population. Results: Four sources accounted for 215 of the 401 sequences. In other words, 54% of all cases were rooted in just five sources. Conclusion: Thus, superspreading seems to have a major impact on the proportion of individuals in a population affected with COVID.
CitationJungck , J. R., & Ko , H. (2023). Phylogenetic Analysis to Detect COVID Superspreaders. Microbiology Research Journal International, 33(8), 36–43. https://doi.org/10.9734/mrji/2023/v33i81400
ISSN2456-7043
URLhttps://udspace.udel.edu/handle/19716/33946
Languageen_US
PublisherMicrobiology Research Journal International
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
KeywordsCOVID
Keywordssuperspreaders
Keywordsphylogenetic networks
TitlePhylogenetic Analysis to Detect COVID Superspreaders
TypeArticle
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