Genomic surveillance identifies SARS-CoV-2 transmission patterns in local university populations, Wisconsin, USA, 2020–2022. Issue 3 (31st March 2023) - Ramaiah, Arunachalam; Khubbar, Manjeet; Bauer, Amy; Scott, Samantha; Lentz, Jennifer; Akinyemi, Katherine; Skillman, Addie; Weiner, Joshua; Balakrishnan, Nandhakumar; Bhattacharyya, Sanjib
Abstract : Novel variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continue to emerge as the coronavirus disease 2019 (COVID-19) pandemic extends into its fourth year. Understanding SARS-CoV-2 circulation in university populations is vital for effective interventions in higher education settings and will inform public health policy during pandemics. In this study, we performed whole-genome sequencing of 537 of 1717 SARS-CoV-2-positive nasopharyngeal/nasal swab samples collected over a nearly 20-month period from two university populations in Wisconsin, USA. 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 accounted for 91 %, while the remaining belonged to BA.2 and its six descendent lineages. Independent analysis of the sequences from these two universities revealed significant differences in the circulating SARS-CoV-2 variants. Phylogenetic analysis of university sequences with a global sub-dataset demonstrated that the sequences of the same lineages from the university populations were more closely related. Genome-based analysis of closely related strains, along with phylogenetic clusters and mutational differences, identified that potential virus transmission occurred within and between universities, as well as between the university and the local community. Although this study improves our understanding of the distinct transmission patterns of circulating variants in local universities, expanding genomic surveillance capacity will aid local jurisdictions not only in identifying emerging SARS-CoV-2 variants, but also in improving data-driven public health mitigation and policy efforts.