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. 2021 Mar 24:taab044. doi: 10.1093/jtm/taab044

Epidemiological, clinical and genomic snapshot of the first 100 B.1.1.7 SARS-CoV-2 cases in Madrid

Laura Pérez-Lago 1,2, Pedro J Sola Campoy 3,4, Sergio Buenestado-Serrano 5,6, Pilar Catalán Pharmacy 7,8,9, Agustín Estévez 10,11, Víctor Manuel de la Cueva Technician 12,13, Mariana G López 14, Marta Herranz Tecnichian 15,16,17, Julia Suárez-González 18,19, Luis Alcalá 20,21,22, Iñaki Comas 23,24, Fernando González-Candelas 25,26, Patricia Muñoz 27,28,29,30, Darío García de Viedma 31,32,33,, on behalf of Gregorio Marañón Microbiology-ID COVID-19 Study Group
PMCID: PMC8083647  PMID: 33763697

Abstract

We present clinical, genomic and epidemiological data on the first 106 cases with the SARS-CoV-2 B.1.1.7 variant in Madrid. Even from the start, the increase of this variant was due to transmission events within the community, some causing extensive clusters, rather than further imports. Most cases developed non-severe disease.

Keywords: COVID-19, SARS-CoV-2, B.1.1.7, UK, Spain, travel


A new SARS-CoV-2 variant, B.1.1.7, emerged in September in the UK, and is responsible for 76.6% of COVID-19 cases.1 This variant has also been reported in another 45 countries, 17 of them European.2,3 B.1.1.7 is considered to have higher transmissibility.4 It carries an unusually high number of specific mutations/deletions, 18, mostly non-synonymous and eight concentrate in the S gene,5 including several which might have relevant functional roles. The 69/70 deletion may be associated to immune response evasion6 and the N501Y substitution increases the affinity to the ACE2 receptor.7 These findings have raised the alarm of having to face a new variant with the potential to accelerate the spread of the pandemic. A recent report finds a realistic possibility that B.1.1.7 is associated with an increased risk of death.8

More data on the behaviour of the B.1.1.7 variant once imported are needed to further understand its potential risks at a wider context. The aim of this study was to analyse the first 106 incident COVID-19 cases in Madrid (650 000 inhabitants covered by our tertiary hospital) infected by the B.1.1.7 variant.

We based our initial screening on the S gene signal dropout in the TaqPath assay (ThermoFisher, Waltham, USA) caused by the 69/70 deletion.9 This screening criterion, based on the S signal dropout, has been accepted as a suitable proxy to identify the B.1.1.7 variant, with 100% specificity over the past weeks in the UK.1

Our first B.1.1.7 case was diagnosed at week 52 (21 December 2020). The patient had a previous positive RT-PCR on December 15. In the month preceding this first B.1.1.7 case, the lineage B.1.177 represented 91% of the SARS-CoV-2 strains in our population and the remaining sequences were distributed among minority lineages (B.2, B.1.1.305, B.1.1.130, B.1.1.29, A.2, B.1.1.1 and B.1.88). Since December 15, and until February 1 (week 2, 2021), we diagnosed 106 COVID-19 cases candidates to be infected by B.1.1.7, and at the time of the writing of this manuscript; B.1.1.7 represents 62% of the total newly diagnosed COVID-19 cases (average value for week 10 [2021]).

Among the B.1.1.7 cases in our study, 51.9% were women and the average age of all study subjects was 40 years (range 1–92). A telephonic survey was performed to collect epidemiological and clinical information from 97 cases (approved by the ethical research committee of Gregorio Marañón Hospital; REF: MICRO.HGUGM.2020-042). Nine cases were asymptomatic, 79 had soft/moderate symptoms, and 14 were hospitalized (10 presented pneumonia, four were admitted to the intensive care unit and two deaths). Most cases (77.3%) had no comorbidities (Table 1).

Table 1.

demographic and clinical features for the B.1.1.7 cases.

Demographics
Average Range
Age 39.63 1–92
Total Percentage (%)
Sex
 Male 51 48.11
 Female 55 51.88
COVID-19 symptoms
 Bilateral pneumonia 10 10.31
 Dyspnea 15 15.46
 Asthenia 28 28.87
 Fever 23 23.71
 General unrest 18 18.55
 Diarrhoea 6 6.19
 Cephalea 20 20.61
 Odinophagia 5 5.15
 Anosmia 16 16.50
 Cough 18 18.55
 Rhinorrhea 10 10.31
 Myalgias 16 16.50
Severity
 Asymptomatic 9 9.28
 Mild 66 68.04
 Intermediate 13 13.40
 Severe 9 9.28
Health care requirement
 Emergency 24 24.74
 Hospital admission 14 14.43
 ICU 4 4.12
Antecedents
 None of interest 75 77.32
 High blood pressure 6 6.18
 COPD 1 1.03
 Asthma 5 5.15
 Diabetes 2 2.06
 Ictus 1 1.03
 Overweight/obesity 2 2.06
 Heart disease 2 2.06
 Autoimmune 1 1.03
 Oncological 2 2.06

Regarding the potential links to the UK, six cases (6.19%) had recently arrived from the UK and all had been diagnosed at the end of December; 12 cases (12.37%) had had contact with people coming from the UK; the remaining 79 cases (81.44%) did not have any direct or indirect links with the UK, indicating a wide community transmission. Moreover, the first case in our study was among those without links to the UK; the partner of this patient, diagnosed the same day, worked in the district tribunal and was in high contact with the general public. This suggests that B.1.1.7 community transmission in Madrid may have started before December 15. The average number of additional cases (either preceding or subsequent) communicated by each B.1.1.7 carrier possibly linked to them was 3.07 [range 0–10].

Whole genome sequencing (WGS, Supplementary Methods) characterization of B.1.1.7 positive specimens could be carried out from 88 cases and in all, the B.1.1.7 variant was confirmed. The phylogenetic analysis (Figure 1a) shows the diversity among the sequences, consistent with the circulating time of this lineage in the UK,10 before being imported to Spain. Moreover, the integration of our WGS data with those from the B.1.1.7 sequences available from the UK in GISAID shows that the cases in Madrid correspond to sequences distributed throughout the tree (Figure 1b). To estimate the most recent common ancestor (MRCA) a multi-sequence alignment was performed, including the 88 B.1.1.7 sequences from Madrid and 67 additional sequences belonging to global SARS-CoV-2 diversity (Supplementary Methods). Before phylogenetic dating, root-to-tip regression of genetic divergence against sampling dates was performed to investigate the molecular clock signal using TempEst v 1.5.3. A strong temporal signal was observed (R2 = 0.83; P > 0.001, slope = 9.83 × 10−4) in root-to-tip analysis, suggesting a strict clock model. The time of most recent common ancestor (MRCA) of Madrid B1.1.7 representatives was estimated in 10 December 2020 [95% HPD 25 November 2020; 24 December 2020]. In this analysis we also estimated the origin of the SARS-CoV-2 pandemic in 8 December 2019 [95% HPD 7 November 2019; 1 January 2020].

Figure 1.

Figure 1

(a) Phylogenetic tree including the 88 sequences from specimens with the B.1.1.7 variant. Clustered cases are indicated with a colour code. Cases with epidemiological relationships are shown with a graphic link between them. The ring surrounding the tree indicates whether the cases had travelled from the UK, showed links to the UK or had no links to the UK. All Madrid sequences were uploaded to GISAID. Supplementary Methods. (b) Integration of the Madrid B.1.1.7 sequences from this study (circles sizes are proportional to the number of cases sharing each genotype) with a random subselection of 394 B.1.1.7 sequences among the 25 624 sequences deposited from the UK in GISAID between 1 December 2020 and 28 January 2021.

Focusing on Madrid sequences, 55 cases (61%) are involved in 14 clusters (0 SNPs between the cases sharing cluster, size 2–14; Figure 1a), suggesting post-importation transmission chains. Four out of the six cases who had arrived from the UK and 10 of the 11 individuals who had links with people recently arrived from that country were involved in some of these clusters (Figure 1a, Supplementary Table 1).

One large cluster (cluster 1), involving 14 cases, was identified, while the remaining clusters were smaller (2–5 cases; Figure 1a). Cluster 1 involved members of four different households (2, 2, 3 and 3 individuals); two of these family units were acquaintances and another two clustered cases knew one individual of one these families (Figure 1a, Supplementary Table 1). The next two largest clusters were number 9 and 16, which included five and six cases. Cluster 9 involved one household (five members) and a 2-year-old case without relationships with those in the cluster. Similarly, cluster 16 included five members from the same household and one unrelated case (Figure 1a). These data exemplify community transmissions in which the same strain is transmitted in different close circles and extends beyond them. Links were also found between the cases included in eight of the smaller clusters (all household-related except two involving close contact between partners or friends, Figure 1a, Supplementary Table 1).

We present valuable clinical and epidemiological data on the first 106 cases diagnosed with the new SARS-CoV-2 B.1.1.7 variant in Madrid that will help understand its dynamics once introduced into a new population. In our context, 16.3% of all newly diagnosed cases carry the B.1.1.7 variant 1 month after its first detection and most developed non-severe disease. The great majority of the cases infected with B.1.1.7, including the first identified case, had no direct or indirect links to the UK. Thus, even from the start, the increase of this variant in our context is mostly due to post-importation transmission events within the community rather than further imports. One of the transmission clusters identified by genomic viral analysis was very large and alerts on the need of more efficient control measures to minimize a rapid transmission of the B.1.1.7 variant.

Author’s contributions

Laboratory experimental tasks: P.C., M.H., V.M.D.C. Bioinformatic analysis: P.J.S.C., S.B.S., L.P.L., M.G.L. Sequencing: J.S.G. Microbiological data and databases: L.A. Clinical/epidemiological research: A.E. Data analysis: D.G.V., L.P.L. Analytical strategies, protocols, pipelines and funding: I.C., F.G.C. MS writing and revisions: D.G.V., L.P.L., P.M.

Conflict of interest

The authors declare no conflict of interest.

Gregorio Marañón Microbiology-ID COVID-19 Study Group

Adán-Jiménez (Javier), Alcalá (Luis), Aldámiz (Teresa), Alonso (Roberto), Álvarez (Beatriz), Álvarez-Uría (Ana), Arroyo (Luis Antonio), Berenguer (Juan), Bermúdez (Elena), Bouza (Emilio), Burillo (Almudena), Candela (Ana), Carrillo (Raquel), Catalán (Pilar), Cercenado (Emilia), Cobos (Alejandro), Díez (Cristina), Escribano (Pilar), Estévez (Agustín), Fanciulli (Chiara), Galar (Alicia), García (Mª Dolores), García de Viedma (Darío), Gijón (Paloma), González (Adolfo), Guillén (Helmuth) Guinea (Jesús), Haces (Laura Vanessa), Herranz (Marta), Kestler (Martha), López (Juan Carlos), Losada (Carmen Narcisa), Machado (Marina), Marín (Mercedes), Martín (Pablo), Montilla (Pedro), Muñoz (Patricia), Olmedo (María), Padilla (Belén), Palomo (María), Parras (Francisco), Pérez-Granda (María Jesús), Pérez-Lago (Laura), Pérez (Leire), R Maus (Sandra), Reigadas (Elena), Rico-Luna (Carla Margarita), Rincón (Cristina), Rodríguez (Belén), Rodríguez (Sara), Rodríguez-Grande (Cristina), Rojas (Adriana), Ruiz-Serrano (María Jesús), Sánchez (Carlos), Sánchez (Mar), Serrano (Julia), Sola Campoy (Pedro J), Tejerina (Francisco), Valerio (Maricela), Veintimilla (Mª Cristina), Vesperinas (Lara), Vicente (Teresa), de la Villa (Sofía).

Funding Information

This work was supported by Instituto de Salud Carlos III (Ref COV20/00140: SeqCOVID—Consorcio para la epidemiología genómica de SARS-CoV-2 en España) and by Consejo Superior de Investigaciones Científicas (CSIC) (PTI Salud Global). LPL holds a Miguel Servet Contract CP15/00075).

Supplementary Material

SupplMethods_taab044
SupplTable_taab044

Acknowledgements

The authors are particularly indebted to Rocío Fernández del Rey and Iván Adán Rodríguez for their efficiency and productivity, instrumental to obtain the clinical and epidemiological data. We are grateful to Dainora Jaloveckas (cienciatraducida.com) for editing and proofreading assistance.

Contributor Information

Laura Pérez-Lago, Servicio de Microbiología Clínica y Enfermedades Infecciosas, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain.

Pedro J Sola Campoy, Servicio de Microbiología Clínica y Enfermedades Infecciosas, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain.

Sergio Buenestado-Serrano, Servicio de Microbiología Clínica y Enfermedades Infecciosas, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain.

Pilar Catalán Pharmacy, Servicio de Microbiología Clínica y Enfermedades Infecciosas, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain; Centro de Investigación Biomédica en Red, Enfermedades Respiratorias (CIBERES), Madrid, Spain.

Agustín Estévez, Servicio de Microbiología Clínica y Enfermedades Infecciosas, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain.

Víctor Manuel de la Cueva Technician, Servicio de Microbiología Clínica y Enfermedades Infecciosas, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain.

Mariana G López, Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia (IBV-CSIC), SeqCOVID Consortium, Valencia, Spain.

Marta Herranz Tecnichian, Servicio de Microbiología Clínica y Enfermedades Infecciosas, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain; Centro de Investigación Biomédica en Red, Enfermedades Respiratorias (CIBERES), Madrid, Spain.

Julia Suárez-González, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain; Genomics Unit, Gregorio Marañón General University Hospital, Madrid, Spain.

Luis Alcalá, Servicio de Microbiología Clínica y Enfermedades Infecciosas, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain; Centro de Investigación Biomédica en Red, Enfermedades Respiratorias (CIBERES), Madrid, Spain.

Iñaki Comas, Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia (IBV-CSIC), SeqCOVID Consortium, Valencia, Spain; Centro de Investigación Biomédica en Red, Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.

Fernando González-Candelas, Centro de Investigación Biomédica en Red, Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Joint Research Unit "Infection and Public Health" FISABIO-University of Valencia, Institute for Integrative Systems Biology (I2SysBio), Valencia, Spain.

Patricia Muñoz, Servicio de Microbiología Clínica y Enfermedades Infecciosas, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain; Centro de Investigación Biomédica en Red, Enfermedades Respiratorias (CIBERES), Madrid, Spain; Departamento de Medicina, Universidad Complutense, Madrid, Spain.

Darío García de Viedma, Servicio de Microbiología Clínica y Enfermedades Infecciosas, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain; Centro de Investigación Biomédica en Red, Enfermedades Respiratorias (CIBERES), Madrid, Spain.

References

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Supplementary Materials

SupplMethods_taab044
SupplTable_taab044

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