SARS-CoV-2 genome sequence prevalence and growth rate update: 5 July 2023
Updated 27 March 2024
Applies to England
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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 16 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 1.21 of the Pangolin data.
As of 22 May 2023, sequencing primers being used by the UK Health Security Agency (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 16 January 2023 to week beginning 12 June 2023, as of 29 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.
We aim to select lineages and/or groups of lineages that are both specific enough to pick up on emerging signals but broad enough to maximise statistical power, and have now updated our approach, which will be used for this and future briefings. Any lineage that has made up more than 1.5% of the total samples and with at least 50 sequenced cases within 6 weeks of the most recent specimen date is modelled separately. Lineages that do not meet these criteria will be iteratively added to a parent lineage until this condition is met, forming new aggregations of parent and child lineages. If the condition cannot be met for a particular lineage (for example, there is no parent lineage at high enough prevalence) it will not be modelled. Lineages with a different high-level parent will never be aggregated together (for example, we will not aggregate BA.2 and BA.5 to B.1.1.529). Unassigned lineages are excluded from this analysis. Note that these aggregations will oftentimes be coarser than in the prevalence plot presented in Figure 1. The relative growth rate of broad lineage classes (for example, parent lineages that include many distant child lineages) will be less informative than explicit modelling specific sublineages. Methods of lineage collapsing for the growth rate analysis are therefore still 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 9 January 2022 to 26 June 2023. 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 with reasonable confidence are EG.1 (37.27%, GAM), XBB.2.3 (21.67%, GAM) and XBB.1.16.1 (15.36%, GAM).
Table 1. Growth rate (GR) of English sequence lineages as of 26 June 2023†
| Lineage* | Lineage group composition** | Pillar 1 sample size*** | Weekly growth rate advantage (GAM) | Estimated prevalence (GAM) | Weekly growth rate advantage (GLM) |
|---|---|---|---|---|---|
| EG.1 (XBB.1.9.2.1) | EG.1 (68.13%); EG.1.3 (21.98%); EG.1.4 (6.59%); EG.1.2 (3.3%) | 91 | 37.27% (95% CI: 10.15 to 64.39) | 10.79% (95% CI: 4.57 to 23.4) | 51.04% (95% CI: 12.62 to 89.46) |
| XBB.2.3 | XBB.2.3 (29.08%); GE.1 (21.99%); XBB.2.3.2 (14.18%); XBB.2.3.11 (9.93%); XBB.2.3.1 (8.51%)… | 141 | 21.67% (95% CI: 20.55 to 22.79) | 13.96% (95% CI: 11.62 to 16.67) | 25.85% (95% CI: -4 to 55.7) |
| XBB.1.9.2 | XBB.1.9.2 (58.96%); EG.5.2 (11.56%); EG.7 (5.78%); EG.5.1.1 (5.2%); EG.6.1 (5.2%)… | 173 | 16% (95% CI: -0.56 to 32.56) | 15.02% (95% CI: 8.37 to 25.48) | 4.69% (95% CI: -21.79 to 31.17) |
| XBB.1.16.1 | XBB.1.16.1 (62.64%); XBB.1.16.11 (15.38%); FU.1 (13.19%); FU.2 (7.69%); XBB.1.16.10 (1.1%) | 91 | 15.36% (95% CI: 1.66 to 29.05) | 7.06% (95% CI: 3.79 to 12.8) | 8.08% (95% CI: -30.21 to 46.37) |
| XBB.1 | FY.1.2 (20.3%); XBB.1.22.1 (11.28%); XBB.1.11.1 (9.77%); XBB.1.17.1 (9.02%); FY.4.1 (8.27%)… | 133 | 13.82% (95% CI: -11.05 to 38.69) | 10.14% (95% CI: 4.62 to 20.82) | 19.33% (95% CI: -13.14 to 51.8) |
| XBB.1.9.1 | XBB.1.9.1 (46.24%); FL.4 (12.19%); FL.9 (5.73%); FL.2 (5.02%); FL.12 (4.3%)… | 279 | 8.33% (95% CI: -5.31 to 21.98) | 15.6% (95% CI: 9.85 to 23.81) | -5.02% (95% CI: -27.82 to 17.77) |
| XBB.1.16 | XBB.1.16 (86.47%); XBB.1.16.2 (7.16%); XBB.1.16.3 (2.65%); XBB.1.16.5 (1.59%); XBB.1.16.4 (0.53%)… | 377 | -11.64% (95% CI: -29.04 to 5.75) | 15.25% (95% CI: 8.8 to 25.14) | -7.09% (95% CI: -29.26 to 15.07) |
| BA.2 | DV.7 (28.4%); DV.6 (11.11%); CH.1.1.1 (11.11%); BA.2 (9.88%); CH.1.1 (4.94%)… | 81 | -14.55% (95% CI: -40.89 to 11.79) | 2.08% (95% CI: 0.81 to 5.22) | -4.16% (95% CI: -56.66 to 48.33) |
| XBB.1.5 | XBB.1.5 (50.82%); XBB.1.5.18 (3.9%); XBB.1.5.13 (3.9%); XBB.1.5.7 (3.75%); XBB.1.5.37 (2.7%)… | 667 | -25.1% (95% CI: -40.73 to -9.48) | 14.47% (95% CI: 9.2 to 22.04) | -16.8% (95% CI: -35.98 to 2.38) |
| FL.3 (XBB.1.9.1.3) | FL.3 (43.4%); FL.3.1 (43.4%); FL.3.2 (7.55%); FL.3.3 (4.72%); FL.3.4 (0.94%) | 106 | -49.3% (95% CI: -130.18 to 31.58) | 0.9% (95% CI: 0.09 to 7.95) | -11.04% (95% CI: -13.64 to -8.45 |
*Listed parent lineages include all sub-lineages, other than those explicitly modelled.
** The top 5 contributing lineages to the modelled group in the most recent 6 weeks (15 May 2022 to 26 June 2023). More than 5 sublineages are indicated by “…”
** Sample size is for Pillar 1 samples in England in the most recent 6 weeks (15 May 2022 to 26 June 2023).
† Sampling range for both logistic regression and generalised additive models (GAM) is from 9 January 2022 to 26 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 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