Jungck, John R. and Ko, Hajae (2023) Phylogenetic Analysis to Detect COVID Superspreaders. Microbiology Research Journal International, 33 (8). pp. 36-43. ISSN 2456-7043
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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.
Item Type: | Article |
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Subjects: | STM Open Academic > Biological Science |
Depositing User: | Unnamed user with email admin@eprint.stmopenacademic.com |
Date Deposited: | 13 Oct 2023 05:18 |
Last Modified: | 13 Oct 2023 05:18 |
URI: | http://publish.sub7journal.com/id/eprint/1281 |