Phylogenetic Analysis to Detect COVID Superspreaders
Date
2023-10-12
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Microbiology Research Journal International
Abstract
Aims: 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.
Description
This 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.
Keywords
COVID, superspreaders, phylogenetic networks
Citation
Jungck , 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