Introduction
Emerging lineages of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have the potential to place significant pressure on public health systems due to increased infectivity, transmissibility, virulence, immune escape or other fitness advantage [Reference Dubey1, Reference Mukherjee and Satardekar2]. Global genomic surveillance has identified >1700 SARS-CoV-2 lineages since the beginning of the COVID-19 pandemic [Reference O'Toole3, 4], of which Alpha (B.1.1.7 and Q), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2 and AY) and Omicron (B.1.1.529 and BA) have been designated as variants of concern by the World Health Organization (WHO) on account of their global public health significance [5]. Additional lineages are currently classified on the basis of properties that are suggestive of an emerging (variants of interest) or possible future (variants under monitoring) risk to global public health [5]. The risk is well illustrated by the recent and rapid emergence of Omicron as the dominant variant in the UK, South Africa and the USA, among other countries, in late November and December 2021 [6–Reference Latif8].
One important epidemiological facet of an emerging SARS-CoV-2 lineage is its propensity to grow in a defined population [Reference Ward9]. There are well-established methods for assessing the rate of temporal growth by, for example, examining the trajectory of case doubling times or estimating the basic reproduction number, R 0, of the agent in question [10, 11]. Viewed from a geographical perspective, these measures are essentially aspatial in that they provide very little information on the geographical growth, or spatial expansion, of the associated infection wave. To extend the examination of SARS-CoV-2 growth rates into the spatial domain, the present paper applies a robust method of spatial epidemiological analysis that is known as the swash-backwash model of the single epidemic wave [Reference Cliff and Haggett12] to the genomic surveillance records of the COVID-19 Genomics UK (COG-UK) Consortium [13]. Using the spatial sequence of detection of sample variants as a proxy for the spatial wave front of infection, our examination yields estimates of the spatial growth rate of multiple SARS-CoV-2 lineages in the local authority areas of England, September 2020–December 2021.
For a total of 37 sample lineages under investigation, we present evidence of a substantial (7.6-fold) difference in the average rate of spatial growth, from the slowest (Delta AY.4.3) to the fastest (Omicron BA.1). Whilst the overall results for the Alpha, Delta and Omicron variants are consistent with the documented growth advantages for these lineages, several emergent Delta sublineages (AY.4.2, AY.43, AY.98 and AY.120) are found to have had a statistically significant growth advantage over the parent lineage. To our knowledge, this is the first comparative study of the spatial growth rate of multiple emerging SARS-CoV-2 lineages at the national level. It is also the first report of a spatial growth advantage for the Delta AY.43, AY.98 and AY.120 lineages, and the first to document an apparently reduced spatial growth rate for a substantial number of other AY lineages that emerged in the spring and summer of 2021. The modelling of spatial growth rates is equally applicable to the analysis of RT-PCR gene target data, and we suggest it to be a potentially valuable adjunct to outbreak response procedures for SARS-CoV-2 variants in a defined population.
Data and methods
Since September 2020, successive waves of SARS-CoV-2 infection with emerging lineages of the Alpha (September 2020 onset), Delta (March 2021 onset) and Omicron (November 2021 onset) variants have been recorded in England [Reference Davies14–16]. The weekly record of COVID-19 cases to mid-December 2021 is plotted in Figure 1a, whilst the underpinning sequence of variants is depicted in Figure 1b. As Figure 1b shows, Alpha, Delta and Omicron achieved the status of dominant variants in December 2020, May 2021 and December 2021, respectively.
Fig. 1. COVID-19 cases in England, September 2020–December 2021. (a) Positive COVID-19 test specimens as recorded by the UK Government. (b) Number of sample genomes of SARS-CoV-2 in the COG-UK database by variant to 18 December 2021. All data are plotted by week of sample collection. Sources: data from GOV.UK Coronavirus (COVID-19) in the UK [17] and COVID-19 Genomics UK (COG-UK) Consortium [18].
Data
We draw on the integrated national-level SARS-CoV-2 genomic surveillance records of the COG-UK Consortium [13]. These records are based on unselected (random sample) sequencing of positive SARS-CoV-2 test samples that have been identified through standard (‘pillar 2’) diagnostic pathways in the UK. Lineages are assigned using the Phylogenetic Assignment of Named Global Outbreak Lineages (pangolin) tool, with lineage counts made available by local authority area and week of sample collection. For further information on the data under examination, see COG-UK Consortium, COVID-19 Genomic Surveillance [18].
Lineage counts for England were accessed from the COG-UK website [18] for a 68-week period, September 2020 (epidemiological week 36, ending 5 September) to December 2021 (epidemiological week 50, ending 18 December) (Fig. 1b). The data set included geo-coded information on 979 075 SARS-CoV-2 samples assigned to the 309 Lower Tier Local Authority (LTLA) divisions of England. Here, we define the 309 LTLAs according to their most recent (May 2021) status. Information on the lineage of 20 655 samples (2.1%) was either suppressed (1105) or not recorded (19 550). Of the remaining 958 420 samples, the majority (93.8%) were classified as belonging to the B.1.1.7 and Q (Alpha, 153 405 samples), B.1.617.2 and AY (Delta, 722 133 samples) and B.1.1.529 and BA (Omicron, 23 137 samples) lineages (Table 1). Samples belonging to these lineages form the basis of all our analysis.
Table 1. Estimated rate of spatial growth ($\bar{t}_{LE}$
) of sample SARS-CoV-2 lineages in England, September 2020–December 2021
a Excludes 1105 detections for which lineage data are suppressed and 19 550 detections for which lineage data are not available.
b Epidemiological week/year, with the last day of the week given in parentheses.
c Excludes a lone detection in week 43 (30 October).
d Indexed to week 47; $\bar{t}_{LE}$
= 6.62 (6.53, 6.70) when indexed to week 43.
Methods
To assess the spatial growth rate of a given SARS-CoV-2 lineage, we draw on the swash-backwash model of the single epidemic wave [Reference Cliff and Haggett12]. In essence, the model represents a spatial derivative of the generic SIR mass action models of infectious disease transmission [Reference Anderson and May19]. Using the binary (presence/absence) of a disease, the model (i) allows the disaggregation of an infection wave into phases of spatial expansion and retreat and (ii) provides a means of measuring the phase transitions of geographical units from susceptible S, through infective I to recovered R status. See, for example, Smallman-Raynor and Cliff [Reference Smallman-Raynor and Cliff20] and Smallman-Raynor et al. [Reference Smallman-Raynor, Cliff and Stickler21].
Measuring the spatial growth rate
Full details of the modelling procedure are outlined by Cliff and Haggett [Reference Cliff and Haggett12]. For the purposes of the present analysis, we focus on the spatial expansion phase (i.e. the change of state from S to I across a set geographical units) for a given SARS-CoV-2 lineage. Specifically, let the first week in which the lineage was detected in England be coded as t = 1. Subsequent weeks were then coded serially as t = 2, 3, …, T, where T is the number of weekly periods from the beginning to the end of the detected occurrence of the lineage. For any given geographical unit, we refer to the first week in which the lineage was detected as the leading edge (LE) of the infection wave. The average time (in weeks) to the detection of the lineage across the set of units can then be defined by a time-weighted mean, $\bar{t}_{LE}$
, of the form
Here, n t is the number of units whose leading edge, LE, occurred in week t and $N = \mathop \sum n_t$
. Formed in this manner, SARS-CoV-2 lineages with relatively high rates of spatial expansion (or rapidly developing LE) take on relatively low values of $\bar{t}_{LE}$
(i.e. short average times to detection). Conversely, lineages with relatively low rates of spatial expansion (or slowly developing LE) take on relatively high values of $\bar{t}_{LE}$
(i.e. long average times to detection).
Application of the model
Equation (1) was used to estimate the spatial growth rate of sample SARS-CoV-2 lineages for which the earliest detection in England occurred in the time period covered by the dataset (September 2020–December 2021) and for which substantial geographical spread had been documented. To ensure the inclusion of sufficient observations for geographical analysis, the sample was limited to lineages that had been detected in at least one-third of the 309 LTLAs by December 2021. Based on these criteria, the sample consisted of 37 lineages. Summary details of the sample, including the number of LTLAs in which each lineage was detected, the total count of detections over the study period and the earliest date of detection, are provided in Table 1.
For each lineage, equation (1) was fitted with t = 1 set to the week of earliest detection in Table 1. In the instance of Omicron, retrospective analysis has identified a lone detection of the BA.1 lineage in epidemiological week 43 of 2021 (week ending 30 October), 4 weeks prior to the subsequent detection and apparent onset of widespread transmission of the variant in epidemiological week 47 (week ending 27 November). For the purposes of the present analysis, we set week 47 as t = 1 for Omicron, but we also report the computed value of $\bar{t}_{LE}$
based on the earlier detection in week 43. Finally, we exclude two LTLAs (City of London and Isles of Scilly) from all analysis on account of the suppression of lineage data due to their small populations. Data analysis was performed in Minitab®17 (Minitab Inc., Pennsylvania, USA) and data mapping in QGIS 3.10.14-A Coruña (QGIS.org) using Local Authority Districts (May 2021) UK and Regions (December 2020) EN shapefiles from the Office for National Statistics (ONS) [22].
Results
Table 1 confirms that the 37 sample lineages were geographically extensive in their transmission, with 29 having been detected in >150 LTLAs, 21 in >250 LTLAs, 16 in >300 LTLAs and nine in the complete set of 307 LTLAs under examination. The majority (23) were associated with >1000 detections, 13 with > 10 000 detections and three with > 100 000 detections. Delta (B.1.617.2 and AY) was the most common lineage (722 133 detections) and AY.4 the most common sublineage (547 403 detections), with AY lineages accounting for 33 of the spread events under examination. In turn, the majority of lineages emerged (as judged by the date of earliest detections) in the spring and summer of 2021, as the Delta infection wave was evolving both domestically and internationally.
Spatial growth curves and leading edge (LE) maps
The upper graphs in Figure 2 plot the count of LTLAs by week of earliest detection of the Alpha (B.1.1.7 and Q), Delta (B.1.617.2 and AY) and Omicron (B.1.1.529 and BA) variants, where weeks are indexed to the earliest detection of the respective variants (Table 1). The lower graphs are spatial growth curves, formed by replotting the information in the upper graphs as a cumulative proportion of LTLAs. Average curves for the set of sample lineages in Table 1 are shown for reference.
Fig. 2. Spatial leading edges (LE) of the Alpha (B.1.1.7 and Q), Delta (B.1.617.2 and AY) and Omicron (B.1.1.529 and BA) variants in England, September 2020–December 2021. The graphs plot, on a weekly basis, the non-cumulative count (upper) and cumulative proportion (lower) of LTLAs in which each of the three variants was first detected. The horizontal (time) axes are indexed to the epidemiological week of first detection (t = 1) of the corresponding variant. Average curves, formed across the set of sample lineages in Table 1, are plotted for reference.
Together, the graphs in Figure 2 portray the temporal development of the spatial leading edges (LE) for each variant. The geographical expression of these LE is captured by the choropleth maps in Figure 3 which plot the week of earliest detection of each variant in the set of LTLAs. The sequentially more rapid spatial growth of the variants (Alpha → Delta → Omicron) is evidenced by the sequentially steeper spatial growth curves (Fig. 2) and the sequentially shorter periods to earliest detection (Fig. 3). The latter feature is emphasised when earliest detections are formed as regional averages in Figure 4.
Fig. 3. Spatial leading edges (LE) of the Alpha (B.1.1.7 and Q), Delta (B.1.617.2 and AY) and Omicron (B.1.1.529 and BA) variants in the LTLAs of England, September 2020–December 2021. Maps are indexed to the epidemiological week of first detection (= week 1) of the corresponding variant and plot the number of weeks to first detection in each LTLA.
Fig. 4. Spatial leadings edges (LE) of the Alpha (B.1.1.7 and Q), Delta (B.1.617.2 and AY) and Omicron (B.1.1.529 and BA) variants in the nine standard regions of England, September 2020–December 2021. Maps plot the average time (in weeks) to first detection of a given variant in each regional subset of LTLAs.
Rates of spatial growth ($\bar{t}_{LE}$
)
The right-hand column in Table 1 summarises the results of the application of equation (1) to each of the sample lineages. Computed values of $\bar{t}_{LE}$
and associated 95% confidence intervals (95% CI) are given, along with an overall average value of $\bar{t}_{LE}$
for the entire sample. As noted above, lineages with relatively high rates of spatial expansion (or rapidly developing LE) are represented by relatively low values of $\bar{t}_{LE}$
(i.e. short average times to detection), while lineages with relatively low rates of spatial expansion (or slowly developing LE) take on relatively high values of $\bar{t}_{LE}$
(i.e. long average times to detection). In this manner, the table confirms the sequential increase in the spatial growth rate for Alpha, Delta and Omicron. On average, the earliest detection of the Alpha variant in a given LTLA occurred at $\bar{t}_{LE} = 9.90$
(95% CI 9.56–10.23) weeks after the earliest sampled detection in England. This reduced to 7.40 (95% CI 7.12–7.68) weeks for Delta and 2.63 (95% CI 2.56–2.71) weeks for Omicron.
Delta AY lineages
Figure 5 is based on the information in Table 1 and plots the values of $\bar{t}_{LE}$
for B.1.617.2 and AY lineages in order, from the lowest (left, high values of $\bar{t}_{LE}$
) to the highest (right, low values of $\bar{t}_{LE}$
) rates of spatial growth. Values are plotted on an inverted vertical scale to facilitate interpretation. The average value of $\bar{t}_{LE}$
, formed across the sample set of lineages in Table 1, is indicated for reference as are the $\bar{t}_{LE}$
for the Alpha (B.1.1.7 and Q), Delta (B.1.617.2 and AY) and Omicron (B.1.1.529 and BA) variants. Spatial growth curves, formed in the manner of Figure 2, are plotted for a sample of 20 AY lineages with relatively high and low rates of spatial growth in Figure 6.
Fig. 5. Estimated rate of spatial growth of sample SARS-CoV-2 lineages in England, September 2020–December 2021. The graph plots values of $\bar{t}_{LE}$
and associated 95% CI from Table 1. Values are ordered from the lowest (left, high values of $\bar{t}_{LE}$
) to the highest (right, low values of $\bar{t}_{LE}$
) rates of spatial growth. Values are plotted on an inverted vertical scale to facilitate interpretation. The average value of $\bar{t}_{LE}$
for the sample is shown for reference.
Fig. 6. Spatial growth curves for sample Delta sublineages in England, March–December 2021. Curves have been formed in the manner of the lower graphs in Figure 2, with the average curve plotted for reference. Lineages are ordered according to the values of $\bar{t}_{LE}$
in Table 1 and are defined as having relatively high (i.e. low values of $\bar{t}_{LE}$
; upper graphs, a) and relatively low (i.e. high values of $\bar{t}_{LE}$
; lower graphs, b) rates of spatial growth.
There is a 7.6-fold difference in the range of values of $\bar{t}_{LE}$
in Figure 5, from Delta AY.4.3 with the lowest spatial growth rate (19.93 weeks) to Omicron with the highest (2.63 weeks). A group of four AY lineages (AY.4.2, AY.43, AY.98 and AY.120), first detected in the period from mid-May to mid-July 2021, are positioned between Delta and Omicron in Figure 5 and display rates of spatial growth that are significantly higher (as judged by 95% CI) than the aggregate rate for the Delta variant. In contrast, the overwhelming majority of AY lineages display statistically lower – in many instances substantially lower – spatial growth rates (as judged by 95% CI) than the aggregate rate for the Delta variant.
Discussion
Recent experience has underscored the importance of the ongoing tracking, monitoring and analysis of emerging SARS-CoV-2 lineages with a view to mitigating the impacts of the COVID-19 pandemic [Reference Angeletti23]. We have used a robust model of spatial epidemiological analysis to estimate the rate of spatial growth of multiple lineages of the virus in England over a 68-week period, September 2020–December 2021. We have shown that the Alpha, Delta and Omicron variants took an average of 9.90, 7.40 and 2.63 weeks, respectively, to reach the set of LTLAs under examination (Table 1 and Fig. 5). Expressed in relative terms, the leading spatial edges were 1.34× faster (Delta vs. Alpha), 2.81× faster (Omicron vs. Delta) and 3.76× faster (Omicron vs. Alpha). Our estimates scale to the approximate length of time that Alpha (12 weeks), Delta (8 weeks) and Omicron (3 weeks) took to establish themselves as the dominant variants in England [18], and are consistent with evidence for the fitness advantage of Delta over Alpha and Omicron over Delta [11, Reference He24, Reference Mahase25].
Of the 121 Delta AY lineages detected in England to December 2021 and included in the genomic surveillance records of the COG-UK Consortium, 33 lineages met the geographical criterion for inclusion in the current analysis. In interpreting the results for these lineages, we note that AY designations are phylogenetically defined and do not necessarily denote any fundamental biological differences between the lineages [26]. Moreover, results of the type documented in this paper are context dependent and cannot be interpreted as evidence of a change in biological transmissibility, immune escape or other fitness advantage. Subject to these caveats, we have identified four AY lineages (AY.4.2, AY.43, AY.98 and AY.120) for which the rate of spatial growth exceeded the aggregate rate for the Delta variant. These lineages had been detected in all (AY.4.2, AY.43 and AY.98) or most (AY.120) of the local authority areas under investigation, and each had been associated with considerably more than 10 000 detections (Table 1). Table 2 summarises the global status of these four lineages as of 9 January 2022. With the exception of the AY.43 lineage, which was prevalent in a number of European countries and associated with >267 000 detections worldwide, the majority of detections of these lineages originated from the UK.
Table 2. Worldwide detection of sample Delta AY lineages with relatively high estimated rates of spatial growth (status: 9 January 2022)
Sources: data from cov-lineages.org [27] and Latif et al. [Reference Latif28–Reference Latif31].
Our findings for the AY.4.2 and AY.43 lineages are consistent with their respective designations by the UK Health Security Agency as a distinct variant under investigation (VUI-21OCT-01) and a variant of concern [32, 33]. Preliminary investigations indicated the AY.4.2 lineage to be associated with a higher growth rate and a higher household secondary attack rate, but with no significant reduction in vaccine effectiveness, as compared to the parent lineage [32, Reference Le Page34]. Although the factors underpinning the higher growth rate of AY.4.2 remain to be established [32, 35, 36], we observe that this lineage accounted for a maximum of 24.4% of all detections (week ending 4 December 2021) before being outcompeted by Omicron [18]. Similarly, the status of the AY.43 lineage in terms of transmission advantage and/or immune escape remains to be determined, although further investigation is merited as new AY.43 sublineages have recently been reported from Brazil [Reference Lima37]. Finally, our identification of a rapid rate of spatial growth for the AY.98 and AY.120 lineages, approximating the estimated rates for AY.4.2 and AY.43, is noteworthy. Whilst very little has been documented on the epidemiological facets of these lineages, both have been identified in a number of countries in Europe and elsewhere (Table 2) and would seem to merit further investigation on the basis of the findings presented here.
With the foregoing exceptions, our analysis has shown that many emerging AY lineages in England in the spring and summer of 2021 were associated with spatial growth rates that were lower (in some instances, substantially lower) than the aggregate rate for the Delta variant (Table 1 and Fig. 5). Multiple biological (e.g. reduced infectivity or transmissibility) and contextual (e.g. progressive expansion of the national COVID-19 vaccination programme) factors may account for this observation. Importantly, there is no evidence of a temporal trend in the observed rates of spatial growth that would be suggestive of either (i) a biological selection pressure in favour of a growth advantage of emerging lineages or (ii) a progressive contextual effect in the form of, for example, increasing levels of vaccination coverage or natural immunity that would serve to retard growth rates.
It is important to emphasise the broader societal and epidemiological context to the spread of SARS-CoV-2 lineages that will have influenced our estimates of $\bar{t}_{LE}$
in Table 1 and Figure 5. For the time period covered by the present study, non-pharmaceutical interventions (NPIs) included: a tier system of local lockdown in October 2020; two periods of national lockdown (November–December 2020 and January–March 2021); a phased lifting of national restrictions in the period to July 2021; and the implementation of ‘Plan B’ control guidelines against the Omicron variant in December 2021 [Reference Brown and Kirk-Wade38]. Whilst the phases of national lockdown had significant impacts on population mobility, mixing and associated opportunities for SARS-CoV-2 transmission [Reference Shepherd39], it is noteworthy that the majority (27) of lineages included in the present analysis were first detected in the period from May to July 2021 (Table 1). This corresponded with the final steps in the Government's four-stage roadmap for the lifting of lockdown measures and was marked by a substantial easing and eventual removal of restrictions on social mixing [Reference Brown and Kirk-Wade38]. To set against this easing of restrictions, lineage growth rates will have been retarded to an unknown extent by the immunity afforded by prior infection with antigenically similar SARS-CoV-2 variants (B.1.617.2 and AY sublineages, in particular) and by the phased rollout of the national COVID-19 vaccination programme [40].
The results we have presented are subject to the limitations of the available lineage data. Although the COG-UK Consortium genomic surveillance data are recognised for their extent and reliability [Reference Robishaw41], the data are formed as a sample of positive SARS-CoV-2 test results and are subject to the limitations and biases of sample data. In this context, we note that the cumulative coverage of the COG-UK records for England was estimated at 13.7% of people with positive SARS-CoV-2 test results to October 2021 [42]. We also note that the sample test data are derived from a laboratory system with testing capabilities that vary by region and time period [Reference Ward9]. Such space-time variations have potentially important implications for analyses, of the type outlined in the present paper, that are dependent on the dates of first detection of SARS-CoV-2 lineages in a multi-region setting.
Our results are also subject to the underpinning assumptions of the analytical procedure. In particular, the computation of $\bar{t}_{LE}$
is dependent on the specification of the index week (i.e. the week that a given lineage was first detected in England) and the degree to which this reflects the date of actual emergence of the lineage in England. The extent to which the sample data accurately track the spatial expansion of the LE for a given lineage, the variable contributions of international travel- and community-related transmission to the development of the LE, and the geographical starting point(s) of a given lineage in the national transmission network, will also have influenced our results in unknown ways. For example, the early involvement and high degree of geographical connectivity of London and the South East may have served to accelerate the spatial transmission of the Alpha variant in the latter months of 2020 [Reference Davies14]. The observed rapid spread of the Delta variant may reflect international importations and onwards transmission from multiple different geographical locations in the spring of 2021 [Reference Mishra15, Reference McCrone43], whilst early cases of the Omicron variant were observed in highly connected regions at a time of reduced NPIs in November and December 2021 [44].
For the purposes of the present analysis, our application of the swash-backwash model has utilised genomic surveillance data. We note, however, that the modelling approach is equally applicable to the analysis of RT-PCR gene target data. As such, the approach may be used to facilitate timely assessments of the spatial growth of emerging SARS-CoV-2 variants and thereby contribute to rapid outbreak responses [Reference Ward9, 45].
Further insights into the spatial growth and decay of SARS-CoV-2 lineages may be gained by application of the full swash-backwash model, but this is dependent on the substantial spatial retreat of any given lineage from the population. Here we note that, with the exception of AY.10 (last detected in July 2021) and AY.8 and Alpha (B.1.1.7 and Q) (both last detected in August/September 2021), there is evidence of the circulation of all the lineages included in Table 1 in the weeks to December 2021.
We have demonstrated, for the first time, a robust method for assessing and comparing the rate of spatial growth of multiple SARS-CoV-2 lineages in a set of geographical areas. We suggest that this approach represents a potentially valuable adjunct to outbreak response procedures for emerging SARS-CoV-2 variants in a defined population.
Acknowledgements
COG-UK is supported by funding from the Medical Research Council (MRC) part of UK Research & Innovation (UKRI), the National Institute of Health Research (NIHR) [grant code: MC_PC_19027], and Genome Research Limited, operating as the Wellcome Sanger Institute. The authors acknowledge use of data generated through the COVID-19 Genomics Programme funded by the Department of Health and Social Care. The views expressed are those of the author and not necessarily those of the Department of Health and Social Care or PHE or UKHSA.
Conflict of interest
None.
Data availability statement
The data that support the findings of this study are available at Wellcome Sanger Institute COVID–19 Genomic Surveillance (https://covid19.sanger.ac.uk/lineages/raw).
Appendix: The COVID-19 Genomics UK (COG-UK) Consortium
Funding acquisition, Leadership and supervision, Metadata curation, Project administration, Samples and logistics, Sequencing and analysis, Software and analysis tools, and Visualisation:
Samuel C Robson 13, 84
Funding acquisition, Leadership and supervision, Metadata curation, Project administration, Samples and logistics, Sequencing and analysis, and Software and analysis tools:
Thomas R Connor 11, 74 and Nicholas J Loman 43
Leadership and supervision, Metadata curation, Project administration, Samples and logistics, Sequencing and analysis, Software and analysis tools, and Visualisation:
Tanya Golubchik 5
Funding acquisition, Leadership and supervision, Metadata curation, Samples and logistics, Sequencing and analysis, and Visualisation:
Rocio T Martinez Nunez 46
Funding acquisition, Leadership and supervision, Project administration, Samples and logistics, Sequencing and analysis, and Software and analysis tools:
David Bonsall 5
Funding acquisition, Leadership and supervision, Project administration, Sequencing and analysis, Software and analysis tools, and Visualisation:
Andrew Rambaut 104
Funding acquisition, Metadata curation, Project administration, Samples and logistics, Sequencing and analysis, and Software and analysis tools:
Luke B Snell 12
Leadership and supervision, Metadata curation, Project administration, Samples and logistics, Software and analysis tools, and Visualisation:
Rich Livett 116
Funding acquisition, Leadership and supervision, Metadata curation, Project administration, and Samples and logistics:
Catherine Ludden 20, 70
Funding acquisition, Leadership and supervision, Metadata curation, Samples and logistics, and Sequencing and analysis:
Sally Corden 74 and Eleni Nastouli 96, 95, 30
Funding acquisition, Leadership and supervision, Metadata curation, Sequencing and analysis, and Software and analysis tools:
Gaia Nebbia 12
Funding acquisition, Leadership and supervision, Project administration, Samples and logistics, and Sequencing and analysis:
Ian Johnston 116
Leadership and supervision, Metadata curation, Project administration, Samples and logistics, and Sequencing and analysis:
Katrina Lythgoe 5, M. Estee Torok 19, 20 and Ian G Goodfellow 24
Leadership and supervision, Metadata curation, Project administration, Samples and logistics, and Visualisation:
Jacqui A Prieto 97, 82 and Kordo Saeed 97, 83
Leadership and supervision, Metadata curation, Project administration, Sequencing and analysis, and Software and analysis tools:
David K Jackson 116
Leadership and supervision, Metadata curation, Samples and logistics, Sequencing and analysis, and Visualisation:
Catherine Houlihan 96, 94
Leadership and supervision, Metadata curation, Sequencing and analysis, Software and analysis tools, and Visualisation:
Dan Frampton 94, 95
Metadata curation, Project administration, Samples and logistics, Sequencing and analysis, and Software and analysis tools:
William L Hamilton 19 and Adam A Witney 41
Funding acquisition, Samples and logistics, Sequencing and analysis, and Visualisation:
Giselda Bucca 101
Funding acquisition, Leadership and supervision, Metadata curation, and Project administration:
Cassie F Pope 40, 41
Funding acquisition, Leadership and supervision, Metadata curation, and Samples and logistics:
Catherine Moore 74
Funding acquisition, Leadership and supervision, Metadata curation, and Sequencing and analysis:
Emma C Thomson 53
Funding acquisition, Leadership and supervision, Project administration, and Samples and logistics:
Ewan M Harrison 116, 102
Funding acquisition, Leadership and supervision, Sequencing and analysis, and Visualisation:
Colin P Smith 101
Leadership and supervision, Metadata curation, Project administration, and Sequencing and analysis:
Fiona Rogan 77
Leadership and supervision, Metadata curation, Project administration, and Samples and logistics:
Shaun M Beckwith 6, Abigail Murray 6, Dawn Singleton 6, Kirstine Eastick 37, Liz A Sheridan 98, Paul Randell 99, Leigh M Jackson 105, Cristina V Ariani 116 and Sónia Gonçalves 116
Leadership and supervision, Metadata curation, Samples and logistics, and Sequencing and analysis:
Derek J Fairley 3, 77, Matthew W Loose 18 and Joanne Watkins 74
Leadership and supervision, Metadata curation, Samples and logistics, and Visualisation:
Samuel Moses 25, 106
Leadership and supervision, Metadata curation, Sequencing and analysis, and Software and analysis tools:
Sam Nicholls 43, Matthew Bull 74 and Roberto Amato 116
Leadership and supervision, Project administration, Samples and logistics, and Sequencing and analysis:
Darren L Smith 36, 65, 66
Leadership and supervision, Sequencing and analysis, Software and analysis tools, and Visualisation:
David M Aanensen 14, 116 and Jeffrey C Barrett 116
Metadata curation, Project administration, Samples and logistics, and Sequencing and analysis:
Dinesh Aggarwal 20, 116, 70, James G Shepherd 53, Martin D Curran 71 and Surendra Parmar 71
Metadata curation, Project administration, Sequencing and analysis, and Software and analysis tools:
Matthew D Parker 109
Metadata curation, Samples and logistics, Sequencing and analysis, and Software and analysis tools:
Catryn Williams 74
Metadata curation, Samples and logistics, Sequencing and analysis, and Visualisation:
Sharon Glaysher 68
Metadata curation, Sequencing and analysis, Software and analysis tools, and Visualisation:
Anthony P Underwood 14, 116, Matthew Bashton 36, 65, Nicole Pacchiarini 74, Katie F Loveson 84 and Matthew Byott 95, 96
Project administration, Sequencing and analysis, Software and analysis tools, and Visualisation:
Alessandro M Carabelli 20
Funding acquisition, Leadership and supervision, and Metadata curation:
Kate E Templeton 56, 104
Funding acquisition, Leadership and supervision, and Project administration:
Thushan I de Silva 109, Dennis Wang 109, Cordelia F Langford 116 and John Sillitoe 116
Funding acquisition, Leadership and supervision, and Samples and logistics:
Rory N Gunson 55
Funding acquisition, Leadership and supervision, and Sequencing and analysis:
Simon Cottrell 74, Justin O'Grady 75, 103 and Dominic Kwiatkowski 116, 108
Leadership and supervision, Metadata curation, and Project administration:
Patrick J Lillie 37
Leadership and supervision, Metadata curation, and Samples and logistics:
Nicholas Cortes 33, Nathan Moore 33, Claire Thomas 33, Phillipa J Burns 37, Tabitha W Mahungu 80 and Steven Liggett 86
Leadership and supervision, Metadata curation, and Sequencing and analysis:
Angela H Beckett 13, 81 and Matthew TG Holden 73
Leadership and supervision, Project administration, and Samples and logistics:
Lisa J Levett 34, Husam Osman 70, 35 and Mohammed O Hassan-Ibrahim 99
Leadership and supervision, Project administration, and Sequencing and analysis:
David A Simpson 77
Leadership and supervision, Samples and logistics, and Sequencing and analysis:
Meera Chand 72, Ravi K Gupta 102, Alistair C Darby 107 and Steve Paterson 107
Leadership and supervision, Sequencing and analysis, and Software and analysis tools:
Oliver G Pybus 23, Erik M Volz 39, Daniela de Angelis 52, David L Robertson 53, Andrew J Page 75 and Inigo Martincorena 116
Leadership and supervision, Sequencing and analysis, and Visualisation:
Louise Aigrain 116 and Andrew R Bassett 116
Metadata curation, Project administration, and Samples and logistics:
Nick Wong 50, Yusri Taha 89, Michelle J Erkiert 99 and Michael H Spencer Chapman 116, 102
Metadata curation, Project administration, and Sequencing and analysis:
Rebecca Dewar 56 and Martin P McHugh 56, 111
Metadata curation, Project administration, and Software and analysis tools:
Siddharth Mookerjee 38, 57
Metadata curation, Project administration, and Visualisation:
Stephen Aplin 97, Matthew Harvey 97, Thea Sass 97, Helen Umpleby 97 and Helen Wheeler 97
Metadata curation, Samples and logistics, and Sequencing and analysis:
James P McKenna 3, Ben Warne 9, Joshua F Taylor 22, Yasmin Chaudhry 24, Rhys Izuagbe 24, Aminu S Jahun 24, Gregory R Young 36, 65, Claire McMurray 43, Clare M McCann 65, 66, Andrew Nelson 65, 66 and Scott Elliott 68
Metadata curation, Samples and logistics, and Visualisation:
Hannah Lowe 25
Metadata curation, Sequencing and analysis, and Software and analysis tools:
Anna Price 11, Matthew R Crown 65, Sara Rey 74, Sunando Roy 96 and Ben Temperton 105
Metadata curation, Sequencing and analysis, and Visualisation:
Sharif Shaaban 73 and Andrew R Hesketh 101
Project administration, Samples and logistics, and Sequencing and analysis:
Kenneth G Laing 41, Irene M Monahan 41 and Judith Heaney 95, 96, 34
Project administration, Samples and logistics, and Visualisation:
Emanuela Pelosi 97, Siona Silviera 97 and Eleri Wilson-Davies 97
Samples and logistics, Software and analysis tools, and Visualisation:
Helen Fryer 5
Sequencing and analysis, Software and analysis tools, and Visualization:
Helen Adams 4, Louis du Plessis 23, Rob Johnson 39, William T Harvey 53, 42, Joseph Hughes 53, Richard J Orton 53, Lewis G Spurgin 59, Yann Bourgeois 81, Chris Ruis 102, Áine O'Toole 104, Marina Gourtovaia 116 and Theo Sanderson 116
Funding acquisition, and Leadership and supervision:
Christophe Fraser 5, Jonathan Edgeworth 12, Judith Breuer 96, 29, Stephen L Michell 105 and John A Todd 115
Funding acquisition, and Project administration:
Michaela John 10 and David Buck 115
Leadership and supervision, and Metadata curation:
Kavitha Gajee 37 and Gemma L Kay 75
Leadership and supervision, and Project administration:
Sharon J Peacock 20, 70 and David Heyburn 74
Leadership and supervision, and Samples and logistics:
Katie Kitchman 37, Alan McNally 43, 93, David T Pritchard 50, Samir Dervisevic 58, Peter Muir 70, Esther Robinson 70, 35, Barry B Vipond 70, Newara A Ramadan 78, Christopher Jeanes 90, Danni Weldon 116, Jana Catalan 118 and Neil Jones 118
Leadership and supervision, and Sequencing and analysis:
Ana da Silva Filipe 53, Chris Williams 74, Marc Fuchs 77, Julia Miskelly 77, Aaron R Jeffries 105, Karen Oliver 116 and Naomi R Park 116
Metadata curation, and Samples and logistics:
Amy Ash 1, Cherian Koshy 1, Magdalena Barrow 7, Sarah L Buchan 7, Anna Mantzouratou 7, Gemma Clark 15, Christopher W Holmes 16, Sharon Campbell 17, Thomas Davis 21, Ngee Keong Tan 22, Julianne R Brown 29, Kathryn A Harris 29, 2, Stephen P Kidd 33, Paul R Grant 34, Li Xu-McCrae 35, Alison Cox 38, 63, Pinglawathee Madona 38, 63, Marcus Pond 38, 63, Paul A Randell 38, 63, Karen T Withell 48, Cheryl Williams 51, Clive Graham 60, Rebecca Denton-Smith 62, Emma Swindells 62, Robyn Turnbull 62, Tim J Sloan 67, Andrew Bosworth 70, 35, Stephanie Hutchings 70, Hannah M Pymont 70, Anna Casey 76, Liz Ratcliffe 76, Christopher R Jones 79, 105, Bridget A Knight 79, 105, Tanzina Haque 80, Jennifer Hart 80, Dianne Irish-Tavares 80, Eric Witele 80, Craig Mower 86, Louisa K Watson 86, Jennifer Collins 89, Gary Eltringham 89, Dorian Crudgington 98, Ben Macklin 98, Miren Iturriza-Gomara 107, Anita O Lucaci 107 and Patrick C McClure 113
Metadata curation, and Sequencing and analysis:
Matthew Carlile 18, Nadine Holmes 18, Christopher Moore 18, Nathaniel Storey 29, Stefan Rooke 73, Gonzalo Yebra 73, Noel Craine 74, Malorie Perry 74, Nabil-Fareed Alikhan 75, Stephen Bridgett 77, Kate F Cook 84, Christopher Fearn 84, Salman Goudarzi 84, Ronan A Lyons 88, Thomas Williams 104, Sam T Haldenby 107, Jillian Durham 116 and Steven Leonard 116
Metadata curation, and Software and analysis tools:
Robert M Davies 116
Project administration, and Samples and logistics:
Rahul Batra 12, Beth Blane 20, Moira J Spyer 30, 95, 96, Perminder Smith 32, 112, Mehmet Yavus 85, 109, Rachel J Williams 96, Adhyana IK Mahanama 97, Buddhini Samaraweera 97, Sophia T Girgis 102, Samantha E Hansford 109, Angie Green 115, Charlotte Beaver 116, Katherine L Bellis 116, 102, Matthew J Dorman 116, Sally Kay 116, Liam Prestwood 116 and Shavanthi Rajatileka 116
Project administration, and Sequencing and analysis:
Joshua Quick 43
Project administration, and Software and analysis tools:
Radoslaw Poplawski 43
Samples and logistics, and Sequencing and analysis:
Nicola Reynolds 8, Andrew Mack 11, Arthur Morriss 11, Thomas Whalley 11, Bindi Patel 12, Iliana Georgana 24, Myra Hosmillo 24, Malte L Pinckert 24, Joanne Stockton 43, John H Henderson 65, Amy Hollis 65, William Stanley 65, Wen C Yew 65, Richard Myers 72, Alicia Thornton 72, Alexander Adams 74, Tara Annett 74, Hibo Asad 74, Alec Birchley 74, Jason Coombes 74, Johnathan M Evans 74, Laia Fina 74, Bree Gatica-Wilcox 74, Lauren Gilbert 74, Lee Graham 74, Jessica Hey 74, Ember Hilvers 74, Sophie Jones 74, Hannah Jones 74, Sara Kumziene-Summerhayes 74, Caoimhe McKerr 74, Jessica Powell 74, Georgia Pugh 74, Sarah Taylor 74, Alexander J Trotter 75, Charlotte A Williams 96, Leanne M Kermack 102, Benjamin H Foulkes 109, Marta Gallis 109, Hailey R Hornsby 109, Stavroula F Louka 109, Manoj Pohare 109, Paige Wolverson 109, Peijun Zhang 109, George MacIntyre-Cockett 115, Amy Trebes 115, Robin J Moll 116, Lynne Ferguson 117, Emily J Goldstein 117, Alasdair Maclean 117 and Rachael Tomb 117
Samples and logistics, and Software and analysis tools:
Igor Starinskij 53
Sequencing and analysis, and Software and analysis tools:
Laura Thomson 5, Joel Southgate 11, 74, Moritz UG Kraemer 23, Jayna Raghwani 23, Alex E Zarebski 23, Olivia Boyd 39, Lily Geidelberg 39, Chris J Illingworth 52, Chris Jackson 52, David Pascall 52, Sreenu Vattipally 53, Timothy M Freeman 109, Sharon N Hsu 109, Benjamin B Lindsey 109, Keith James 116, Kevin Lewis 116, Gerry Tonkin-Hill 116 and Jaime M Tovar-Corona 116
Sequencing and analysis, and Visualisation:
MacGregor Cox 20
Software and analysis tools, and Visualisation:
Khalil Abudahab 14, 116, Mirko Menegazzo 14, Ben EW Taylor MEng 14, 116, Corin A Yeats 14, Afrida Mukaddas 53, Derek W Wright 53, Leonardo de Oliveira Martins 75, Rachel Colquhoun 104, Verity Hill 104, Ben Jackson 104, JT McCrone 104, Nathan Medd 104, Emily Scher 104 and Jon-Paul Keatley 116
Leadership and supervision:
Tanya Curran 3, Sian Morgan 10, Patrick Maxwell 20, Ken Smith 20, Sahar Eldirdiri 21, Anita Kenyon 21, Alison H Holmes 38, 57, James R Price 38, 57, Tim Wyatt 69, Alison E Mather 75, Timofey Skvortsov 77 and John A Hartley 96
Metadata curation:
Martyn Guest 11, Christine Kitchen 11, Ian Merrick 11, Robert Munn 11, Beatrice Bertolusso 33, Jessica Lynch 33, Gabrielle Vernet 33, Stuart Kirk 34, Elizabeth Wastnedge 56, Rachael Stanley 58, Giles Idle 64, Declan T Bradley 69, 77, Jennifer Poyner 79 and Matilde Mori 110
Project administration:
Owen Jones 11, Victoria Wright 18, Ellena Brooks 20, Carol M Churcher 20, Mireille Fragakis 20, Katerina Galai 20, 70, Andrew Jermy 20, Sarah Judges 20, Georgina M McManus 20, Kim S Smith 20, Elaine Westwick 20, Stephen W Attwood 23, Frances Bolt 38, 57, Alisha Davies 74, Elen De Lacy 74, Fatima Downing 74, Sue Edwards 74, Lizzie Meadows 75, Sarah Jeremiah 97, Nikki Smith 109 and Luke Foulser 116
Samples and logistics:
Themoula Charalampous 12, 46, Amita Patel 12, Louise Berry 15, Tim Boswell 15, Vicki M Fleming 15, Hannah C Howson-Wells 15, Amelia Joseph 15, Manjinder Khakh 15, Michelle M Lister 15, Paul W Bird 16, Karlie Fallon 16, Thomas Helmer 16, Claire L McMurray 16, Mina Odedra 16, Jessica Shaw 16, Julian W Tang 16, Nicholas J Willford 16, Victoria Blakey 17, Veena Raviprakash 17, Nicola Sheriff 17, Lesley-Anne Williams 17, Theresa Feltwell 20, Luke Bedford 26, James S Cargill 27, Warwick Hughes 27, Jonathan Moore 28, Susanne Stonehouse 28, Laura Atkinson 29, Jack CD Lee 29, Dr Divya Shah 29, Adela Alcolea-Medina 32, 112, Natasha Ohemeng-Kumi 32, 112, John Ramble 32, 112, Jasveen Sehmi 32, 112, Rebecca Williams 33, Wendy Chatterton 34, Monika Pusok 34, William Everson 37, Anibolina Castigador 44, Emily Macnaughton 44, Kate El Bouzidi 45, Temi Lampejo 45, Malur Sudhanva 45, Cassie Breen 47, Graciela Sluga 48, Shazaad SY Ahmad 49, 70, Ryan P George 49, Nicholas W Machin 49, 70, Debbie Binns 50, Victoria James 50, Rachel Blacow 55, Lindsay Coupland 58, Louise Smith 59, Edward Barton 60, Debra Padgett 60, Garren Scott 60, Aidan Cross 61, Mariyam Mirfenderesky 61, Jane Greenaway 62, Kevin Cole 64, Phillip Clarke 67, Nichola Duckworth 67, Sarah Walsh 67, Kelly Bicknell 68, Robert Impey 68, Sarah Wyllie 68, Richard Hopes 70, Chloe Bishop 72, Vicki Chalker 72, Ian Harrison 72, Laura Gifford 74, Zoltan Molnar 77, Cressida Auckland 79, Cariad Evans 85, 109, Kate Johnson 85, 109, David G Partridge 85, 109, Mohammad Raza 85, 109, Paul Baker 86, Stephen Bonner 86, Sarah Essex 86, Leanne J Murray 86, Andrew I Lawton 87, Shirelle Burton-Fanning 89, Brendan AI Payne 89, Sheila Waugh 89, Andrea N Gomes 91, Maimuna Kimuli 91, Darren R Murray 91, Paula Ashfield 92, Donald Dobie 92, Fiona Ashford 93, Angus Best 93, Liam Crawford 93, Nicola Cumley 93, Megan Mayhew 93, Oliver Megram 93, Jeremy Mirza 93, Emma Moles-Garcia 93, Benita Percival 93, Megan Driscoll 96, Leah Ensell 96, Helen L Lowe 96, Laurentiu Maftei 96, Matteo Mondani 96, Nicola J Chaloner 99, Benjamin J Cogger 99, Lisa J Easton 99, Hannah Huckson 99, Jonathan Lewis 99, Sarah Lowdon 99, Cassandra S Malone 99, Florence Munemo 99, Manasa Mutingwende 99, Roberto Nicodemi 99, Olga Podplomyk 99, Thomas Somassa 99, Andrew Beggs 100, Alex Richter 100, Claire Cormie 102, Joana Dias 102, Sally Forrest 102, Ellen E Higginson 102, Mailis Maes 102, Jamie Young 102, Rose K Davidson 103, Kathryn A Jackson 107, Lance Turtle 107, Alexander J Keeley 109, Jonathan Ball 113, Timothy Byaruhanga 113, Joseph G Chappell 113, Jayasree Dey 113, Jack D Hill 113, Emily J Park 113, Arezou Fanaie 114, Rachel A Hilson 114, Geraldine Yaze 114 and Stephanie Lo 116
Sequencing and analysis:
Safiah Afifi 10, Robert Beer 10, Joshua Maksimovic 10, Kathryn McCluggage 10, Karla Spellman 10, Catherine Bresner 11, William Fuller 11, Angela Marchbank 11, Trudy Workman 11, Ekaterina Shelest 13, 81, Johnny Debebe 18, Fei Sang 18, Marina Escalera Zamudio 23, Sarah Francois 23, Bernardo Gutierrez 23, Tetyana I Vasylyeva 23, Flavia Flaviani 31, Manon Ragonnet-Cronin 39, Katherine L Smollett 42, Alice Broos 53, Daniel Mair 53, Jenna Nichols 53, Kyriaki Nomikou 53, Lily Tong 53, Ioulia Tsatsani 53, Sarah O'Brien 54, Steven Rushton 54, Roy Sanderson 54, Jon Perkins 55, Seb Cotton 56, Abbie Gallagher 56, Elias Allara 70, 102, Clare Pearson 70, 102, David Bibby 72, Gavin Dabrera 72, Nicholas Ellaby 72, Eileen Gallagher 72, Jonathan Hubb 72, Angie Lackenby 72, David Lee 72, Nikos Manesis 72, Tamyo Mbisa 72, Steven Platt 72, Katherine A Twohig 72, Mari Morgan 74, Alp Aydin 75, David J Baker 75, Ebenezer Foster-Nyarko 75, Sophie J Prosolek 75, Steven Rudder 75, Chris Baxter 77, Sílvia F Carvalho 77, Deborah Lavin 77, Arun Mariappan 77, Clara Radulescu 77, Aditi Singh 77, Miao Tang 77, Helen Morcrette 79, Nadua Bayzid 96, Marius Cotic 96, Carlos E Balcazar 104, Michael D Gallagher 104, Daniel Maloney 104, Thomas D Stanton 104, Kathleen A Williamson 104, Robin Manley 105, Michelle L Michelsen 105, Christine M Sambles 105, David J Studholme 105, Joanna Warwick-Dugdale 105, Richard Eccles 107, Matthew Gemmell 107, Richard Gregory 107, Margaret Hughes 107, Charlotte Nelson 107, Lucille Rainbow 107, Edith E Vamos 107, Hermione J Webster 107, Mark Whitehead 107, Claudia Wierzbicki 107, Adrienn Angyal 109, Luke R Green 109, Max Whiteley 109, Emma Betteridge 116, Iraad F Bronner 116, Ben W Farr 116, Scott Goodwin 116, Stefanie V Lensing 116, Shane A McCarthy 116, 102, Michael A Quail 116, Diana Rajan 116, Nicholas M Redshaw 116, Carol Scott 116, Lesley Shirley 116 and Scott AJ Thurston 116
Software and analysis tools:
Will Rowe 43, Amy Gaskin 74, Thanh Le-Viet 75, James Bonfield 116, Jennifier Liddle 116 and Andrew Whitwham 116
1 Barking, Havering and Redbridge University Hospitals NHS Trust, 2 Barts Health NHS Trust, 3 Belfast Health & Social Care Trust, 4 Betsi Cadwaladr University Health Board, 5 Big Data Institute, Nuffield Department of Medicine, University of Oxford, 6 Blackpool Teaching Hospitals NHS Foundation Trust, 7 Bournemouth University, 8 Cambridge Stem Cell Institute, University of Cambridge, 9 Cambridge University Hospitals NHS Foundation Trust, 10 Cardiff and Vale University Health Board, 11 Cardiff University, 12 Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, Guy's and St Thomas' NHS Foundation Trust, 13 Centre for Enzyme Innovation, University of Portsmouth, 14 Centre for Genomic Pathogen Surveillance, University of Oxford, 15 Clinical Microbiology Department, Queens Medical Centre, Nottingham University Hospitals NHS Trust, 16 Clinical Microbiology, University Hospitals of Leicester NHS Trust, 17 County Durham and Darlington NHS Foundation Trust, 18 Deep Seq, School of Life Sciences, Queens Medical Centre, University of Nottingham, 19 Department of Infectious Diseases and Microbiology, Cambridge University Hospitals NHS Foundation Trust, 20 Department of Medicine, University of Cambridge, 21 Department of Microbiology, Kettering General Hospital, 22 Department of Microbiology, South West London Pathology, 23 Department of Zoology, University of Oxford, 24 Division of Virology, Department of Pathology, University of Cambridge, 25 East Kent Hospitals University NHS Foundation Trust, 26 East Suffolk and North Essex NHS Foundation Trust, 27 East Sussex Healthcare NHS Trust, 28 Gateshead Health NHS Foundation Trust, 29 Great Ormond Street Hospital for Children NHS Foundation Trust, 30 Great Ormond Street Institute of Child Health (GOS ICH), University College London (UCL), 31 Guy's and St. Thomas' Biomedical Research Centre, 32 Guy's and St. Thomas' NHS Foundation Trust, 33 Hampshire Hospitals NHS Foundation Trust, 34 Health Services Laboratories, 35 Heartlands Hospital, Birmingham, 36 Hub for Biotechnology in the Built Environment, Northumbria University, 37 Hull University Teaching Hospitals NHS Trust, 38 Imperial College Healthcare NHS Trust, 39 Imperial College London, 40 Infection Care Group, St George's University Hospitals NHS Foundation Trust, 41 Institute for Infection and Immunity, St George's University of London, 42 Institute of Biodiversity, Animal Health & Comparative Medicine, 43 Institute of Microbiology and Infection, University of Birmingham, 44 Isle of Wight NHS Trust, 45 King's College Hospital NHS Foundation Trust, 46 King's College London, 47 Liverpool Clinical Laboratories, 48 Maidstone and Tunbridge Wells NHS Trust, 49 Manchester University NHS Foundation Trust, 50 Microbiology Department, Buckinghamshire Healthcare NHS Trust, 51 Microbiology, Royal Oldham Hospital, 52 MRC Biostatistics Unit, University of Cambridge, 53 MRC-University of Glasgow Centre for Virus Research, 54 Newcastle University, 55 NHS Greater Glasgow and Clyde, 56 NHS Lothian, 57 NIHR Health Protection Research Unit in HCAI and AMR, Imperial College London, 58 Norfolk and Norwich University Hospitals NHS Foundation Trust, 59 Norfolk County Council, 60 North Cumbria Integrated Care NHS Foundation Trust, 61 North Middlesex University Hospital NHS Trust, 62 North Tees and Hartlepool NHS Foundation Trust, 63 North West London Pathology, 64 Northumbria Healthcare NHS Foundation Trust, 65 Northumbria University, 66 NU-OMICS, Northumbria University, 67 Path Links, Northern Lincolnshire and Goole NHS Foundation Trust, 68 Portsmouth Hospitals University NHS Trust, 69 Public Health Agency, Northern Ireland, 70 Public Health England, 71 Public Health England, Cambridge, 72 Public Health England, Colindale, 73 Public Health Scotland, 74 Public Health Wales, 75 Quadram Institute Bioscience, 76 Queen Elizabeth Hospital, Birmingham, 77 Queen's University Belfast, 78 Royal Brompton and Harefield Hospitals, 79 Royal Devon and Exeter NHS Foundation Trust, 80 Royal Free London NHS Foundation Trust, 81 School of Biological Sciences, University of Portsmouth, 82 School of Health Sciences, University of Southampton, 83 School of Medicine, University of Southampton, 84 School of Pharmacy & Biomedical Sciences, University of Portsmouth, 85 Sheffield Teaching Hospitals NHS Foundation Trust, 86 South Tees Hospitals NHS Foundation Trust, 87 Southwest Pathology Services, 88 Swansea University, 89 The Newcastle upon Tyne Hospitals NHS Foundation Trust, 90 The Queen Elizabeth Hospital King's Lynn NHS Foundation Trust, 91 The Royal Marsden NHS Foundation Trust, 92 The Royal Wolverhampton NHS Trust, 93 Turnkey Laboratory, University of Birmingham, 94 University College London Division of Infection and Immunity, 95 University College London Hospital Advanced Pathogen Diagnostics Unit, 96 University College London Hospitals NHS Foundation Trust, 97 University Hospital Southampton NHS Foundation Trust, 98 University Hospitals Dorset NHS Foundation Trust, 99 University Hospitals Sussex NHS Foundation Trust, 100 University of Birmingham, 101 University of Brighton, 102 University of Cambridge, 103 University of East Anglia, 104 University of Edinburgh, 105 University of Exeter, 106 University of Kent, 107 University of Liverpool, 108 University of Oxford, 109 University of Sheffield, 110 University of Southampton, 111 University of St Andrews, 112 Viapath, Guy's and St Thomas’ NHS Foundation Trust, and King's College Hospital NHS Foundation Trust, 113 Virology, School of Life Sciences, Queens Medical Centre, University of Nottingham, 114 Watford General Hospital, 115 Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, 116 Wellcome Sanger Institute, 117 West of Scotland Specialist Virology Centre, NHS Greater Glasgow and Clyde, 118 Whittington Health NHS Trust.
Open access
