Phylogenetic Analysis to Detect COVID Superspreaders

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
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

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