Genomic surveillance identifies SARS-CoV-2 transmission patterns in local university populations, Wisconsin, USA, 2020–2022
Genomic surveillance identifies SARS-CoV-2 transmission patterns in local university populations, Wisconsin, USA, 2020–2022
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基因组监测确定 2020-2022 年美国威斯康星州当地大学人群中 SARS-CoV-2 的传播模式
DOI:
10.1101/2022.07.06.22277014
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发表时间:
2022
影响因子:
3.9
通讯作者:
S. Bhattacharyya
中科院分区:
文献类型:
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作者:
A. Ramaiah;M. Khubbar;A. Bauer;Samantha J. Scott;J. Lentz;K. Akinyemi;Addie Skillman;Josh Weiner;Nandhakumar Balakrishnan;S. Bhattacharyya
Novel variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continue to emerge as the current coronavirus disease 2019 (COVID-19) pandemic extends into its third year. Understanding SARS-CoV-2 circulation in university populations is vital for effective interventions in a higher education setting that will inform pubic health policy during pandemics. In this study, we performed whole-genome sequencing of 537 of 1,717 SARS-CoV-2 positive nasopharyngeal/nasal swab samples collected for nearly 20 months from the two university populations in Wisconsin, United States. We observed that the viral sequences were distributed into 57 lineages/sub-lineages belonging to 15 clades of which the majority were from 21K (Omicron, 36.13%) and 21J (Delta, 30.91%). Nearly 40% (213) of the sequences were Omicron of which BA.1 and its eight descendent lineages account for 91%, while the remaining belong to BA.2 and its six descendent lineages. The independent analysis of these two universities sequences revealed significant differences in circulating the SARS-CoV-2 variants. The genome-based analysis of closely-related strains along with phylogenetic clusters had identified that potential virus transmission occurred within as well as between universities, and between the university and local community. Although this study improves our understanding of distinct transmission patterns of circulating variants in local universities, expanding the genomic surveillance capacity will aid local jurisdictions in identifying emerging SARS-CoV-2 variants like BA.4 and BA.5, and improve data-driven public health mitigation and policy efforts.