Projects per year
Abstract
Background: The SARS-CoV-2 variant B.1.1.7 was first identified in December, 2020, in England. We aimed to investigate whether increases in the proportion of infections with this variant are associated with differences in symptoms or disease course, reinfection rates, or transmissibility.
Methods: We did an ecological study to examine the association between the regional proportion of infections with the SARS-CoV-2 B.1.1.7 variant and reported symptoms, disease course, rates of reinfection, and transmissibility. Data on types and duration of symptoms were obtained from longitudinal reports from users of the COVID Symptom Study app who reported a positive test for COVID-19 between Sept 28 and Dec 27, 2020 (during which the prevalence of B.1.1.7 increased most notably in parts of the UK). From this dataset, we also estimated the frequency of possible reinfection, defined as the presence of two reported positive tests separated by more than 90 days with a period of reporting no symptoms for more than 7 days before the second positive test. The proportion of SARS-CoV-2 infections with the B.1.1.7 variant across the UK was estimated with use of genomic data from the COVID-19 Genomics UK Consortium and data from Public Health England on spike-gene target failure (a non-specific indicator of the B.1.1.7 variant) in community cases in England. We used linear regression to examine the association between reported symptoms and proportion of B.1.1.7. We assessed the Spearman correlation between the proportion of B.1.1.7 cases and number of reinfections over time, and between the number of positive tests and reinfections. We estimated incidence for B.1.1.7 and previous variants, and compared the effective reproduction number, Rt, for the two incidence estimates.
Findings: From Sept 28 to Dec 27, 2020, positive COVID-19 tests were reported by 36 920 COVID Symptom Study app users whose region was known and who reported as healthy on app sign-up. We found no changes in reported symptoms or disease duration associated with B.1.1.7. For the same period, possible reinfections were identified in 249 (0·7% [95% CI 0·6–0·8]) of 36 509 app users who reported a positive swab test before Oct 1, 2020, but there was no evidence that the frequency of reinfections was higher for the B.1.1.7 variant than for pre-existing variants. Reinfection occurrences were more positively correlated with the overall regional rise in cases (Spearman correlation 0·56–0·69 for South East, London, and East of England) than with the regional increase in the proportion of infections with the B.1.1.7 variant (Spearman correlation 0·38–0·56 in the same regions), suggesting B.1.1.7 does not substantially alter the risk of reinfection. We found a multiplicative increase in the Rt of B.1.1.7 by a factor of 1·35 (95% CI 1·02–1·69) relative to pre-existing variants. However, Rt fell below 1 during regional and national lockdowns, even in regions with high proportions of infections with the B.1.1.7 variant.
Interpretation: The lack of change in symptoms identified in this study indicates that existing testing and surveillance infrastructure do not need to change specifically for the B.1.1.7 variant. In addition, given that there was no apparent increase in the reinfection rate, vaccines are likely to remain effective against the B.1.1.7 variant. Funding: Zoe Global, Department of Health (UK), Wellcome Trust, Engineering and Physical Sciences Research Council (UK), National Institute for Health Research (UK), Medical Research Council (UK), Alzheimer's Society.
Original language | English |
---|---|
Pages (from-to) | e335-e345 |
Number of pages | 11 |
Journal | The Lancet Public Health |
Volume | 6 |
Issue number | 5 |
Early online date | 12 Apr 2021 |
DOIs | |
Publication status | Published - 1 May 2021 |
Keywords
- UKRI
- MRC
- MR/M016560/1
Fingerprint
Dive into the research topics of 'Changes in symptomatology, reinfection, and transmissibility associated with the SARS-CoV-2 variant B.1.1.7: an ecological study'. Together they form a unique fingerprint.Datasets
-
COVID-19 Genomics UK (COG-UK) consortium
Robson, S. (Creator), NCBI, 29 Apr 2020
https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB37886
Dataset
-
Dataset: GISAID EpiCov SARS-CoV-2 Whole Genome Sequencing Database
Robson, S. (Creator), GISAID, 2020
https://www.epicov.org/epi3/frontend#3bd481
Dataset
Projects
- 1 Finished
-
STOP COVID-19: Sequencing and Tracking Of Phylogeny in COVID-19
Robson, S., Scarlett, G., Bourgeois, Y., Beckett, A., Loveson, K., Glaysher, S., Chauhan, A., Goudarzi, S., Cook, K., Fearn, C., Paul, H. & Dent, H.
UK Health Security Agency, COG-UK
1/04/20 → 31/03/23
Project: Research