SARS-CoV-2 genome sequence prevalence and growth rate update: 27 March 2024
Updated 27 March 2024
Applies to England
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Variant prevalence
Testing policy and sequencing should be considered when interpreting variant data. Information about surveillance systems for England are reported by the UK Health Security Agency (UKHSA) in the National Influenza and COVID-19 surveillance report.
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 9 October 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 1.26 of the Pangolin data.
The ‘Other’ category in Figure 1 is comprised primarily of XBB.2 and its sublineages. The lineage no longer meets the required criteria to be included in the plot and recombinant lineages are not collapsed into higher parent lineages (for example, BA.2). This has increased the proportion of sequences categorised as ‘Other’ between the week beginning 9 October 2023 and the week beginning 1 January 2024.
Figure 2 shows the relationship between lineages shown in Figure 1. The figure details the hierarchical relationship of the lineages from left to right, starting with B.1.1.529 through 15 sublineage levels, including aliases and the parent lineages for any recombinants included in the prevalence figures (for example, BJ.1 and BM.1.1.1 are the parent lineages for the XBB recombinant). Lineages that have been combined with a lineage ancestral to B.1.1.529 in Figure 1 are not included in Figure 2. The percentage of each lineage in the most recent week of data in Figure 1 is shown next to the relevant lineage labels in Figure 2. Where a sublineage has a percentage in Figure 2, it is excluded from the percentage given for any parent lineages. Any lineages that are not present in the most recent week of genomic data will not have a percentage in Figure 2. The colours in Figure 2 correspond to the lineage colours in Figure 1.
Figure 1. Prevalence of Pangolin lineages in the UK sequence data with a specimen date from week beginning 9 October 2023 to week beginning 11 March 2024, as of 25 March 2024
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.
Figure 2. Sankey diagram showing the relationship between Pangolin lineages observed in UK sequence data since 9 October 2023
Data shown as of 25 March 2024. Proportions are given for lineages that are observed in sequences with a specimen date between 11 March 2024 and 17 March 2024. Lineage colours match those in Figure 1.
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, which is reflected in larger confidence intervals on the 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. 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 added to a group with their closest parent lineage but will not be aggregated further than one. If the condition cannot be met for a particular lineage (for example, it does not have a close 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 often be broader 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 of specific sublineages. This is why we set a limit of one level when aggregating child lineages into parents. 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 28 September 2023 to 14 March 2024. A lineage is considered to have a growth rate advantage if it has a positive growth rate and 95% confidence intervals (CIs) that do not include zero. There were no lineages with a relative growth rate advantage over other circulating linages (Table 1).
Table 1. Growth rate (GR) of English sequence lineages as of 14 March 2024†
| Lineage* | Lineage Group Composition** | Pillar 1 Sample Size*** | Weekly growth rate advantage (GAM) | Estimated prevalence¥ (GAM) | Weekly growth rate advantage (GLM) |
|---|---|---|---|---|---|
| JN.1.18 (BA.2.86.1.1.18) | JN.1.18 (100%) | 53 | 9.72% (95% CI: -4.29 to 23.74) | 2.76% (95% CI: 1.25 to 5.99) | 17.05% (95% CI: -65.17 to 99.27) |
| JN.1.7 (BA.2.86.1.1.7) | JN.1.7 (89.63%); JN.1.7.1 (9.76%); JN.1.7.2 (0.61%) | 164 | 9.33% (95% CI: -6.8 to 25.45) | 11.2% (95% CI: 6.87 to 17.75) | -7.25% (95% CI: -41.42 to 26.92) |
| JN.1.8 (BA.2.86.1.1.8) | JN.1.8 (55%); JN.1.8.1 (45%) | 60 | 3.25% (95% CI: -10.37 to 16.86) | 2.82% (95% CI: 1.41 to 5.56) | 4.82% (95% CI: -3.46 to 13.1) |
| BA.2.86.1 | BA.2.86.1 (37.35%); JN.2 (21.69%); JN.3 (18.07%); JN.6 (13.25%); JN.4 (8.43%)… | 83 | -1.67% (95% CI: -20.15 to 16.81) | 2.96% (95% CI: 1.38 to 6.22) | -5.07% (95% CI: -60.2 to 50.07) |
| JN.1.4 (BA.2.86.1.1.4) | JN.1.4 (96.78%); JN.1.4.1 (1.88%); JN.1.4.2 (0.8%); JN.1.4.3 (0.54%) | 373 | -6.02% (95% CI: -32.54 to 20.49) | 19.9% (95% CI: 12.7 to 29.79) | 4.22% (95% CI: -27.68 to 36.11) |
| JN.1 (BA.2.86.1.1) | JN.1 (89.72%); JN.1.19 (2.84%); JN.1.16 (2.3%); JN.1.22 (1.99%); JN.1.9 (0.84%)… | 1,304 | -9.57% (95% CI: -22.31 to 3.16) | 44.33% (95% CI: 35.68 to 53.34) | -5.95% (95% CI: -24.6 to 12.69) |
| JN.1.1 (BA.2.86.1.1.1) | JN.1.1 (90.59%); JN.1.1.1 (5.88%); JN.1.1.3 (2.35%); JN.1.1.2 (1.18%) | 85 | -26.69% (95% CI: -51.62 to -1.75) | 1.51% (95% CI: 0.55 to 4.05) | -49.43% (95% CI: -131.87 to 33.01) |
*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 (02 February 2024 to 14 March 2024). More than 5 sublineages are indicated by “…”
*** Sample size is for Pillar 1 samples in England in the most recent 6 weeks (02 February 2024 to 14 March 2024).
¥ Estimated prevalence for the 14 March 2024.
† Sampling range for both logistic regression and GAMs is from 28 September 2023 to 14 March 2024.
CI = confidence intervals
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. International data has been gathered from the Global Initiative on Sharing All Influenza Data (GISAID).
Authors of this report
UKHSA Genomics Public Health Analysis Team
UKHSA Infectious Disease Modelling Team
UKHSA TARZET Technical Secretariat