Scientific Publications
Genomic epidemiology of SARS-CoV-2 in Norfolk, UK, March 2020 – December 2022
Microbial Genomics, 11, 7
In the UK, the COVID-19 Genomics UK Consortium (COG-UK) established a real-time national genomic surveillance system during the COVID-19 pandemic, producing centralized data for monitoring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As a COG-UK partner, Quadram Institute Bioscience in Norfolk sequenced over 87,000 SARS-CoV-2 genomes as part of the national effort, contributing to the region becoming densely sequenced. Retrospective analysis of SARS-CoV-2 lineage dynamics in this region may contribute to preparedness for future pandemics. In total, 29,406 SARS-CoV-2 whole genome sequences and corresponding metadata from Norfolk were extracted from the COG-UK dataset, sampled between March 2020 and December 2022, representing 9.9% of regional COVID-19 cases. Sequences were lineage typed using Pangolin, with subsequent lineage analysis carried out in R using RStudio and related packages, including graphical analysis using ggplot2. In total, 401 global lineages were identified, with 69.8% appearing more than once and 31.2% over ten times. Temporal clustering identified six lineage communities based on first lineage emergence. Alpha, Delta and Omicron variants of concern (VOCs) accounted for 8.6, 34.9 and 48.5% of sequences, respectively. These formed four regional epidemic waves alongside the remaining lineages which were observed in the early pandemic prior to VOC designation and were termed pre-VOC lineages. Regional comparison highlighted variability in VOC epidemic wave dates dependent on location. This study is the first to assess SARS-CoV-2 diversity in Norfolk across a large timescale within the COVID-19 pandemic. SARS-CoV-2 was both highly diverse and dynamic throughout the Norfolk region between March 2020 and December 2022, with a strong VOC presence within the latter two-thirds of the study period. The study also displays the utility of incorporating genomic epidemiological methods into pandemic response.
Microbial Genomics, 11, 7
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