SARS-CoV-2 genome sequence prevalence and growth rate update: 21 June 2023
Updated 8 August 2024
Applies to England
© Crown copyright 2024
This publication is licensed under the terms of the Open Government Licence v3.0 except where otherwise stated. To view this licence, visit nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: psi@nationalarchives.gov.uk.
Where we have identified any third party copyright information you will need to obtain permission from the copyright holders concerned.
This publication is available at https://www.gov.uk/government/publications/sars-cov-2-genome-sequence-prevalence-and-growth-rate/sars-cov-2-genome-sequence-prevalence-and-growth-rate-update-21-june-2023
Variant prevalence
Testing policy and sequencing should be considered when interpreting variant data and is described in full in the variant technical briefing series. The prevalence of lineages amongst UK sequences by Phylogenetic Assignment of Named Global Outbreak Lineages (Pangolin) designation is presented in Figure 1. Lineages are shown if there are more than or equal to 5,000 sequences since 2 January 2023 or if they represent more than or equal to 1% of sequences within a single week over the last 6 weeks. Lineages that do not meet these criteria are combined with their parent lineage (for example, BA.2.4 is combined with BA.2).
The lineages have been assigned using the accurate Ultrafast Sample placement on Existing tRee (UShER) mode and version 1.20 of the Pangolin data.
As of 22 May 2023, sequencing primers being used by UKHSA were changed from ARTIC v4.1 to ARTIC v5.3.2 and this may impact lineage designation of sequences.
Figure 1. Prevalence of Pangolin lineages in the UK sequence data with a specimen date from week beginning 2 January 2023 to week beginning 29 May 2023, as of 16 June 2023
The total number of valid sequence results per week is shown by the black line. The ‘Other’ category in this plot contains all lineages that do not meet the relevant criteria after combining smaller sub-lineages. ‘Unassigned’ are sequences that could not be assigned a lineage by Pangolin. Lineages present in at least 2% of sequences in the most recent week are labelled to the right of the plot.
Variant modelling
Two models are currently used to estimate the growth advantage of emerging lineages: a logistic regression generalised linear model (GLM) and a generalised additive model (GAM). Models are fit to a geographically stratified sample of Pillar 1 cases to ensure that relative growth rates are estimated in relation to a local set of co-circulating lineages. Tests associated with travel are excluded. A full description of methods can be found in the variant technical briefing series. In recent months there are fewer tests to model than earlier in the year (see Figure 1). This is due to reduced sampling effort and lower prevalence. Moreover, the proliferation of lineages to monitor means that sample sizes for specific lineages can be small. Uncertainty in our modelled relative growth rates is therefore increased, as is apparent with larger confidence intervals on estimates. Methods of lineage collapsing for use in growth rate analysis are in the process of being refined.
Growth rates were based on sequences sampled through Pillar 1 testing (primarily positive tests conducted in hospital) in England (Table 1). The sampling range for both the logistic regression GLM and GAM is from 26 December 2022 to 12 June 2023. Any group of lineages shown in the plot above were modelled provided there were at least 50 sequenced cases in the sample set. The model fit for any lineage with a positive growth rate advantage (with 95% confidence intervals (CIs) that do not cross zero) are shown in Figure 2. The lineages with positive growth rate are XBB.1.9.2 (47.08%, GAM), B.1.1.529 (23.73%, GAM), XBB.2.3 (19.05%, GAM), XBB.1.9.1 (17.04%, GAM), XBB.1.16.1 (16.35, GAM). Note that a previously monitored lineage XBB.1.16 is now stable at what is likely a peak prevalence of approximately 15.71%.
Table 1. Growth rate (GR) of English sequence lineages as of 12 June 2023†
| Lineage* | Pillar 1 sample size** | Weekly growth rate advantage (GAM) | Estimated prevalence (GAM) | Weekly growth rate advantage (GLM) |
|---|---|---|---|---|
| XBB.1.9.2 | 802 | 47.08% (95% CI: 26.42 to 67.73) | 16.85% (95% CI: 8.51 to 30.63) | 31.79% (95% CI: 8.37 to 55.21) |
| B.1.1.529 | 3,975 | 23.73% (95% CI: 1.92 to 45.53) | 3.29% (95% CI: 1.42 to 7.41) | -3.79% (95% CI: -51.86 to 44.29) |
| XBB.1.5.59 | 88 | 19.68% (95% CI: -75.99 to 115.35) | 0.82% (95% CI: 0.03 to 18.91) | -111.47% (95% CI: -245.22 to 22.27) |
| XBB.2.3 | 342 | 19.05% (95% CI: 17.61 to 20.49) | 6.35% (95% CI: 2.84 to 13.6) | 25.85% (95% CI: -4.25 to 55.95) |
| FL.1 | 76 | 18.01% (95% CI: -17.9 to 53.92) | 0.45% (95% CI: 0.01 to 14.72) | 19.52% (95% CI: -80.86 to 119.9) |
| XBB.1.9.1 | 1,841 | 17.04% (95% CI: 1.68 to 32.41) | 10.77% (95% CI: 6.44 to 17.46) | 13.78% (95% CI: -8.16 to 35.73) |
| XBB.1.16.1 | 156 | 16.35% (95% CI: 3.07 to 29.63) | 5.39% (95% CI: 2.99 to 9.53) | 26.1% (95% CI: -6.07 to 58.27) |
| XBB.1 | 642 | 12.52% (95% CI: -12.39 to 37.43) | 2.92% (95% CI: 1.17 to 7.09) | 33.94% (95% CI: -6.17 to 74.06) |
| CH.1.1 | 4,893 | 11.16% (95% CI: -16.11 to 38.44) | 1.72% (95% CI: 0.61 to 4.76) | 15.77% (95% CI: -37.71 to 69.25) |
| Other | 1,156 | 9.91% (95% CI: -25.58 to 45.41) | 1.4% (95% CI: 0.41 to 4.68) | -1.49% (95% CI: -68.29 to 65.31) |
| EG.1 | 276 | 9.38% (95% CI: -10.79 to 29.55) | 3.3% (95% CI: 1.55 to 6.89) | 5.69% (95% CI: -29.81 to 41.19) |
| FL.4 | 202 | 5.68% (95% CI: -16.13 to 27.49) | 2.35% (95% CI: 0.98 to 5.52) | 45.47% (95% CI: -0.24 to 91.17) |
| XBB.1.5.24 | 346 | 3% (95% CI: -38.39 to 44.39) | 0.81% (95% CI: 0.18 to 3.59) | -3.61% (95% CI: -10.4 to 3.19) |
| XBB.1.16 | 601 | 1.85% (95% CI: -13.57 to 17.28) | 15.71% (95% CI: 9.08 to 25.81) | 3.27% (95% CI: -13.3 to 19.85) |
| FL.2 | 156 | -2.94% (95% CI: -29.13 to 23.25) | 1.09% (95% CI: 0.37 to 3.16) | -0.45% (95% CI: -55.12 to 54.22) |
| Unassigned | 2,199 | -5.99% (95% CI: -15.81 to 3.82) | 2.4% (95% CI: 1.49 to 3.84) | 0.51% (95% CI: -36.42 to 37.44) |
| FL.3.2 | 208 | -6.66% (95% CI: -28.73 to 15.41) | 0.33% (95% CI: 0.07 to 1.51) | -20.67% (95% CI: -120.69 to 79.34) |
| XBB.1.16.2 | 56 | -6.67% (95% CI: -56.68 to 43.33) | 1.07% (95% CI: 0.2 to 5.58) | 6.23% (95% CI: -45.03 to 57.48) |
| EU.1.1 | 53 | -7.26% (95% CI: -39.93 to 25.4) | 0.48% (95% CI: 0.12 to 1.93) | -3.9% (95% CI: -83.97 to 76.17) |
| XBB.1.5.52 | 331 | -9.47% (95% CI: -24.91 to 5.97) | 0.76% (95% CI: 0.33 to 1.74) | -9.99% (95% CI: -13.8 to -6.18) |
| XBB.1.5.18 | 1,294 | -10.41% (95% CI: -39.34 to 18.53) | 0.81% (95% CI: 0.26 to 2.54) | -18.46% (95% CI: -75.23 to 38.31) |
| XBB.1.5 | 12,493 | -14.54% (95% CI: -30.61 to 1.53) | 16.11% (95% CI: 10.4 to 24.11) | -16.75% (95% CI: -30.9 to -2.6) |
| XBB.1.5.13 | 508 | -17.91% (95% CI: -49.05 to 13.24) | 1.13% (95% CI: 0.41 to 3.1) | 10.38% (95% CI: -39.79 to 60.55) |
| FL.9 | 164 | -19.12% (95% CI: -51 to 12.76) | 0.5% (95% CI: 0.14 to 1.73) | -31.54% (95% CI: -106.81 to 43.73) |
| XBB.1.17.1 | 191 | -19.48% (95% CI: -70 to 31.03) | 0.46% (95% CI: 0.08 to 2.45) | 8.4% (95% CI: -58.87 to 75.67) |
| BQ.1.1 | 8,014 | -20.83% (95% CI: -63.41 to 21.75) | 0.12% (95% CI: 0.01 to 0.98) | -36.49% (95% CI: -143.71 to 70.73) |
| CH.1.1.1 | 3,708 | -24.03% (95% CI: -46.14 to -1.91) | 1.05% (95% CI: 0.45 to 2.42) | -42.39% (95% CI: -85.85 to 1.07) |
| FL.5 | 178 | -24.99% (95% CI: -66.15 to 16.17) | 0.2% (95% CI: 0.03 to 1.27) | 29.2% (95% CI: -72.28 to 130.67) |
| XBB.2.3.2 | 56 | -26.11% (95% CI: -73.49 to 21.27) | 0.33% (95% CI: 0.06 to 1.98) | -46.67% (95% CI: -126.3 to 32.96) |
| XBB.1.5.47 | 435 | -27.54% (95% CI: -92.83 to 37.74) | 0.22% (95% CI: 0.04 to 1.27) | 34.58% (95% CI: -75.76 to 144.91) |
| XBB.1.5.15 | 370 | -28.25% (95% CI: -68.19 to 11.68) | 0.27% (95% CI: 0.05 to 1.4) | -65.55% (95% CI: -163.47 to 32.38) |
| FL.3.1 | 505 | -28.77% (95% CI: -56.57 to -0.96) | 1.14% (95% CI: 0.42 to 3) | -29.38% (95% CI: -74.13 to 15.37) |
| XBB.1.5.7 | 2,074 | -29.41% (95% CI: -62.4 to 3.58) | 0.58% (95% CI: 0.17 to 1.97) | -29.07% (95% CI: -80.91 to 22.77) |
| XBB.1.5.12 | 197 | -30.05% (95% CI: -67.35 to 7.26) | 0.31% (95% CI: 0.06 to 1.51) | -57.19% (95% CI: -154.52 to 40.14) |
| XBB.1.5.37 | 154 | -36.61% (95% CI: -90.01 to 16.78) | 0.3% (95% CI: 0.04 to 2.09) | -44.43% (95% CI: -118.62 to 29.75) |
| FL.3 | 1,388 | -39.11% (95% CI: -121.91 to 43.68) | 1.1% (95% CI: 0.12 to 9.53) | -6.2% (95% CI: -45.6 to 33.19) |
| EM.1 | 429 | -41.29% (95% CI: -84.89 to 2.32) | 0.23% (95% CI: 0.05 to 1.07) | -83.8% (95% CI: -184.05 to 16.45) |
| EG.1.3 | 183 | -59.8% (95% CI: -149.15 to 29.54) | 0.11% (95% CI: 0.01 to 2.41) | -121.7% (95% CI: -216.8 to -26.59) |
| FL.3.3 | 247 | -98.73% (95% CI: -211.17 to 13.71) | 0% (95% CI: 0 to 1.15) | -299.16% (95% CI: -631.21 to 32.89) |
*Listed parent lineages include all sub-lineages, other than those explicitly modelled. ‘Unassigned’ are sequences that could not be assigned a lineage by Pangolin.
** Sample size is for Pillar 1 samples in England from 26 December 2022 to 12 June 2023.
† Sampling range for both logistic regression and generalised additive models (GAM) is from 26 December 2022 to 12 June 2023.
CI = confidence intervals
Figure 2. Modelled prevalence of lineage groups with a growth rate advantage over other circulating lineages
The black line shows the central estimate and blue shaded regions the 95% confidence intervals. Points show the national level proportions, with the size indicative of sample numbers for that particular lineage. The grey portion of the ribbon denotes that this period of time is likely to be backfilled with more sequenced cases, making proportions unreliable.
Sources and acknowledgments
Data sources
Data used in this investigation is derived from the Cloud Infrastructure for Microbial Bioinformatics (CLIMB) data set, the UK Health Security Agency (UKHSA) genomic programme data set and the UKHSA Second Generation Surveillance System.
Authors of this report
UKHSA Genomics Public Health Analysis Team
UKHSA Infectious Disease Modelling Team