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. 2023 Oct 25;290(2009):20231284.
doi: 10.1098/rspb.2023.1284. Epub 2023 Oct 18.

Lineage replacement and evolution captured by 3 years of the United Kingdom Coronavirus (COVID-19) Infection Survey

Affiliations

Lineage replacement and evolution captured by 3 years of the United Kingdom Coronavirus (COVID-19) Infection Survey

Katrina A Lythgoe et al. Proc Biol Sci. .

Abstract

The Office for National Statistics Coronavirus (COVID-19) Infection Survey (ONS-CIS) is the largest surveillance study of SARS-CoV-2 positivity in the community, and collected data on the United Kingdom (UK) epidemic from April 2020 until March 2023 before being paused. Here, we report on the epidemiological and evolutionary dynamics of SARS-CoV-2 determined by analysing the sequenced samples collected by the ONS-CIS during this period. We observed a series of sweeps or partial sweeps, with each sweeping lineage having a distinct growth advantage compared to their predecessors, although this was also accompanied by a gradual fall in average viral burdens from June 2021 to March 2023. The sweeps also generated an alternating pattern in which most samples had either S-gene target failure (SGTF) or non-SGTF over time. Evolution was characterized by steadily increasing divergence and diversity within lineages, but with step increases in divergence associated with each sweeping major lineage. This led to a faster overall rate of evolution when measured at the between-lineage level compared to within lineages, and fluctuating levels of diversity. These observations highlight the value of viral sequencing integrated into community surveillance studies to monitor the viral epidemiology and evolution of SARS-CoV-2, and potentially other pathogens.

Keywords: Coronavirus (COVID-19) Infection Survey; SARS-CoV-2; United Kingdom; evolution.

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Conflict of interest statement

We declare we have no competing interests.

Figures

Figure 1.
Figure 1.
Lineage dynamics and genetic diversity through time. (a) Proportion of swabs taken as part of the ONS-CIS that were positive with Ct ≤ 30 (electronic supplementary material, figure S1), with bars coloured by the proportion of sequenced samples belonging to each major lineage. (b) Number of variant of concern (VOC) sequenced samples from rarer lineages (i.e. not classified as a major lineage). (c) Per day growth rate advantage of each of the major lineages compared to all other contemporary samples. BA.4 and BA.5 were considered together due to their concordant trajectories, and uncertainty is represented by 200 data bootstraps. The horizontal lines represent how long it would take the VOC prevalence to double (14 days, dashed; 7 days dotted; 2 days dot-dash). (d) Proportion of sequenced samples with both the given lineage and S-gene target failure (SGTF) pattern. The bold markers represent the proportion of sequenced samples that were of both the indicated lineage and had S-gene target failure (SGTF) during RT-PCR testing. The pale markers indicate those samples that were non-SGTF. (e) Genetic diversity among all samples and among samples of the same major lineages. Lineage designations include all sub-lineages except where indicated, and all samples were grouped by the week in which they were collected, with the date giving the first day of the collection week (every third week labelled for clarity).
Figure 2.
Figure 2.
Dated phylogeny and root-to-tip distance of ONS-CIS sequences. A maximum likelihood phylogeny of 3000 ONS sequences with over 95% genome coverage was generated using IQ-TREE (electronic supplementary material, figure S3). The samples were chosen using weighted random sampling, ensuring the major lineages and the rarely samples VOCs were as evenly distributed through time as possible Top. Root to tip distance for samples from the maximum likelihood phylogeny. BA.2 sequences (excluding BA.2.75) collected before and after 12 September 2022 were considered separately; the later sequences are all from the BA.2.3.20 lineage or its descendents. Bottom. Time tree generated from the maximum likelihood phylogeny using TreeTime [45].

References

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