University of Birmingham
Changes in symptomatology, reinfection, and
transmissibility associated with the SARS-CoV-2
variant B.1.1.7
COVID-19 Genomics UK (COG-UK) Consortium
DOI:
10.1016/s2468-2667(21)00055-4
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COVID-19 Genomics UK (COG-UK) Consortium 2021, 'Changes in symptomatology, reinfection, and
transmissibility associated with the SARS-CoV-2 variant B.1.1.7: an ecological study', The Lancet. Public health,
vol. 6, no. 5, pp. e335-e345. https://doi.org/10.1016/s2468-2667(21)00055-4
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Download date: 09. Aug. 2022
www.thelancet.com/public-health Vol 6 May 2021
e335
Articles
Lancet Public Health 2021;
6: e335–45
Published Online
April 12, 2021
https://doi.org/10.1016/
S2468-2667(21)00055-4
See Comment page e267
*Contributed equally
†Members are listed in the
appendix (pp 11–18)
‡Contributed equally
School of Biomedical
Engineering and Imaging
Sciences (M S Graham PhD,
C H Sudre PhD, M Antonelli PhD,
B Murray MSc, T Varsavsky MSc,
K Kläser MSc, L S Canas PhD,
E Molteni PhD, M Modat PhD,
Prof A Hammers PhD,
Prof S Ourselin PhD) and
Department of Twin Research
and Genetic Epidemiology
(Prof T D Spector PhD,
C J Steves PhD), King’s College
London, London, UK; MRC Unit
for Lifelong Health and Ageing,
Department of Population
Science and Experimental
Medicine (C H Sudre) and Centre
for Medical Image Computing,
Department of Computer
Science (C H Sudre), University
College London, London, UK;
Zoe Global, London, UK
(A May MA, L Polidori MSc,
S Selvachandran MSc, C Hu MA,
J Capdevila PhD, J Wolf MA);
Clinical and Translational
Epidemiology Unit,
Massachusetts General Hospital
and Harvard Medical School,
Boston, MA, USA
(D A Drew PhD, L H Nguyen MD,
Prof A T Chan MD)
Correspondence to:
Dr Mark Graham, School of
Biomedical Engineering and
Imaging Sciences, King’s College
London, London SE1 7EH, UK
mark.graham@kcl.ac.uk
See Online for appendix
Changes in symptomatology, reinfection, and transmissibility
associated with the SARS-CoV-2 variant B.1.1.7:
an ecological study
Mark S Graham*, Carole H Sudre*, Anna May, Michela Antonelli, Benjamin Murray, Thomas Varsavsky, Kerstin Kläser, Liane S Canas,
Erika Molteni, Marc Modat, David A Drew, Long H Nguyen, Lorenzo Polidori, Somesh Selvachandran, Christina Hu, Joan Capdevila, COVID-19
Genomics UK (COG-UK) Consortium†, Alexander Hammers, Andrew T Chan, Jonathan Wolf, Tim D Spector, Claire J Steves‡, Sebastien Ourselin‡
Summary
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 dierences 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 eective reproduction number, R
t
, 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 R
t
of B.1.1.7 by a factor of 1·35 (95% CI 1·02–1·69)
relative to pre-existing variants. However, R
t
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 eective 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.
Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4·0 license.
Introduction
In early December, 2020, a phylogenetically distinct
cluster of SARS-CoV-2 was genetically characterised in
the southeast of England. Most cases had been detected
in November, with a small number detected as early as
September, 2020.
1
Genomic surveillance revealed that this
new variant, termed B.1.1.7, has several mutations of
immuno logical significance and has been spreading
rapidly, with cases increasing in frequency.
2
It is important
to under stand how these mutations could aect the
presenta tion and spread of COVID-19 so that eective
public health responses can be formulated.
3
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Preliminary evidence from epidemiological studies
suggests that the B.1.1.7 variant is more transmissible
than pre-existing variants. Davies and colleagues
4
found
the B.1.1.7 variant to be 43–90% (95% CI 38–130) more
transmissible than pre-existing variants, and Volz and
colleagues have shown that the B.1.1.7 variant increases
the eective reproduction number, R
t
, by a factor of
1·5–2·0.
5
Evidence suggests that the B.1.1.7 variant
increases the risk of admission to hospital and death.
6
However, much is still unknown. From a public health
perspective, it is crucial to understand whether the
B.1.1.7 variant necessitates changes to existing measures
for disease monitoring and containment. For instance,
changes to symptomatology could require modi fications
to symptomatic testing programmes to ensure that new
cases are identified, and changes to disease duration could
require changes in the duration of isolation required for
infected individuals. It is important for modelling and
forecasting to understand whether the B.1.1.7 variant
alters the rate of reinfection. Early estimates of the
transmissibility of the B.1.1.7 variant are uncertain and
additional estimates using independent data sources are
needed. Furthermore, it is important to understand how
these findings will aect measures to control the spread of
the pandemic using non-pharmaceutical inter ventions,
such as lockdowns.
We aimed to investigate the symptomatology, disease
course, rates of reinfection, and transmissibility of the
B.1.1.7 variant in the UK population.
Methods
Study design and participants
We did ecological studies to assess the symptoms, disease
course, rates of reinfection, and transmissibility associ-
ated with increasing proportions of infections with the
B.1.1.7 variant in the UK population. We used data from
the COVID Symptom Study,
7
a longitudinal dataset
providing symptom reports and test results from a
population of more than 4 million adults living in the UK,
in combination with surveillance data from the COVID-19
Genomics UK (COG-UK) Consortium
8
and a spike-gene
target failure (SGTF) correlate in community testing data.
The study was approved by the King’s College London
Ethics Committee (REMAS ID 18210, review refer ence
LRS-19/20-18210). All participants provided consent
through the COVID Symptom Study app.
Data sources
Longitudinal data were prospectively collected through
the COVID Symptom Study app, developed by Zoe Global
with input from King’s College London (London, UK),
Massachusetts General Hospital (Boston, MA, USA), and
Lund and Uppsala Universities (Sweden). The app
7
guides
users through a set of enrolment questions, establishing
baseline demographic and health information. Users are
asked to record each day whether they feel physically
normal and, if not, to log any symptoms. After a user
reports any symptoms, they are asked “Where are you
right now?”, with the options “At home”, “At hospital with
Research in context
Evidence before this study
To identify existing evidence on the SARS-CoV-2 B.1.1.7 variant,
we searched PubMed and Google Scholar for articles published
between Dec 1, 2020, and Feb 1, 2021, using the keywords
“COVID-19” AND “B.1.1.7” with no language restrictions,
finding 281 results. We did not find any studies that
investigated B.1.1.7-associated changes in symptoms or their
severity and duration, but found one study showing that the
B.1.1.7 variant did not change the ratio of symptomatic to
asymptomatic infections. We found six articles describing
laboratory-based investigations of the responses of the
B.1.1.7 variant to vaccine-induced immunity, but no work
investigating what this means for natural immunity and the
likelihood of reinfection outside the laboratory. We found
five articles that showed increased transmissibility of the
B.1.1.7 variant. Other identified studies were not relevant.
Added value of this study
To our knowledge, this is the first study to explore changes in
symptom type and duration and community reinfection rates
associated with the B.1.1.7 variant. We used self-reported
symptom logs from 36 920 users of the COVID Symptom Study
app who reported positive test results between Sept 28 and
Dec 27, 2020. The B.1.1.7 variant was not associated with
changes in the COVID-19 symptoms reported, nor their
duration. We also did not find evidence for an increase in
reinfections in the presence of the B.1.1.7 variant. We found a
multiplicative increase in the effective reproduction number, R
t
,
of the B.1.1.7 variant by a factor of 1·35 (95% CI 1·02–1·69)
compared with pre-existing variants. However, we found that R
t
fell below 1 during regional and national lockdowns, even in
regions with high proportions of infections with the
B.1.1.7 variant.
Implications of all the available evidence
Our findings suggest that existing criteria for symptomatic
COVID-19 testing need not change as a result of the increase in
infections with the B.1.1.7 variant. Building on the results of
laboratory studies, the finding that reinfection is not more likely
in the presence of the B.1.1.7 variant suggests that immunity
developed from infection with pre-existing variants is likely to
protect against the B.1.1.7 variant and that vaccines will
probably remain effective against this new variant. Our results
add to the emerging consensus that the B.1.1.7 variant has
increased transmissibility. The finding that R
t
fell below 1 during
regional and national lockdowns, even in regions with high
levels of infection with the B.1.1.7 variant, requires further
investigation to establish the factors that enabled this decrease
and thus to inform countries seeking to control the spread of
the B.1.1.7 variant
.
Articles
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e337
suspected COVID-19 symptoms”, or “Back from hospital”.
Users are also asked to maintain a record of any COVID-19
tests and their date, type, and result in the app. Users are
able to record the same data on behalf of others, such as
family members, to increase data coverage among those
unlikely to use mobile apps, such as older adults. We
included users living in the UK who had logged responses
through the app at least once in the period between
Sept 28 and Dec 27, 2020.
We used data released on Jan 13, 2021, from COG-UK
to extract time-series of the percentage of daily cases
resulting from the B.1.1.7 variant in Scotland, Wales, and
each of the seven National Health Service (NHS) regions
in England. Northern Ireland was excluded because of
the low number of samples in the COG-UK dataset.
These data were produced by sequencing a sample of
PCR tests done in the community. Because of a delay of
around 2 weeks
2
between PCR tests and genomic
sequencing, we used data only from samples taken up to
Dec 31, 2020, to avoid censoring eects.
Additionally, we used data from Public Health England
on the probable new variant captured in community cases
in England according to SGTF. One of the spike gene
mutations in the B.1.1.7 variant has been observed to cause
an SGTF in the test used in three of England’s large
laboratories for the analysis of community cases.
1
This
failure results in a marker that is sensitive, but not
necessarily specific, to the B.1.1.7 variant, as other
circulating variants also contain the mutation leading to an
SGTF. Comparison with genomic data shows that, from
Nov 30, 2020, onwards, more than 96% of cases with the
SGTF were from the B.1.1.7 variant.
9
The propor tion of
SGTF cases is made available in England for each of the
316 lower-tier local authorities. We grouped these data into
each NHS region using a population-weighted average to
enable integration with other data sources.
Statistical analysis
To assess whether the symptomatology of infection with
the B.1.1.7 variant diered from that of previous variants,
we investigated the change in symptom reporting from
Sept 28 to Dec 27, 2020, covering 13 complete weeks over
the period when the proportion of infections with the
B.1.1.7 variant grew most notably in the NHS regions of
London, South East, and East of England. For each week,
in every region considered, we calculated the proportion
of users reporting each symptom. Users were included in
a week if they had reported a positive swab result (by PCR
or lateral-flow test) in the period 14 days before or after
that week. For each region and symptom, we did a linear
regression, examining the association between infections
and the B.1.1.7 variant as a proportion of total SARS-CoV-2
infections in that region (independent variable) and the
proportion of users reporting the symp tom (dependent
variable) over the 13 weeks considered. We adjusted for
the age and sex of users, as well as for two seasonal
environmental confounders: regional tempera ture and
humidity. Seasonal confounders were calculated each day
from the temperature and relative humidity at 2 m above
the surface (obtained from NASA climate data), averaged
across each region considered.
We also examined the association between the proportion
of infections with the B.1.1.7 variant and the disease
burden, measured here as the total number of dierent
symptoms reported over a period of 2 weeks before and
2 weeks after the test, and the relation with asymptomatic
infection, defined as users reporting a positive test result
but no symptoms in the 2 weeks before or after the test.
We also investigated the rate of self-reported hospital visits,
including users who reported being in hospital with
suspected COVID-19 symptoms or being back from hos-
pital. We also investigated the proportion of individuals
reporting a long duration of symptoms, using a previously
published definition of continuous symptoms reported for
at least 28 days.
10
To avoid censoring eects, the analyses
of admission to hospital and long symptom duration
included symptom reports to Jan 18, 2021, and the analysis
of long symptom duration also considered reports of
positive tests up to Dec 21, 2020. All analyses were adjusted
for sex, age, temperature, and humidity. We controlled for
the false discovery rate to account for multiple comparisons.
We defined possible reinfection 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. We calculated the
proportion of possible reinfections among individuals
who reported their first positive test before Oct 1, 2020. To
assess whether the risk of reinfection was stronger in the
presence of the B.1.1.7 variant, we did ecological studies
in every region, examining the Spearman correlation
between the proportion of infections with the B.1.1.7
variant and the number of reinfections over time, and
between the proportion of positive tests reported through
the app and the number of reinfections. We compared
these two corre lations in each region with use of the
Mann-Whitney U test.
Daily estimates of the incidence of SARS-CoV-2 infection
in Scotland, Wales, and each of the seven NHS regions in
England during the period from Oct 1 to Dec 27, 2020,
were produced using data from the COVID Symptom
Study app and previously described methods.
11
Using the
COG-UK data to estimate the proportion of infections with
the B.1.1.7 variant in each region per day, these incidence
estimates were decomposed into two incidence time-series
per region, one for pre-existing variants and one for B.1.1.7,
with the constraint that the two time-series should sum to
match the total incidence. R
t
was estimated separately
for the pre-existing variants and B.1.1.7 using previously
described methods.
11
Briefly, we used the relationship
I
t+1
=I
t
exp(μ[R
t
– 1]), where 1/μ is the serial interval and I
t
the
incidence on day t. We modelled the system as a Poisson
process and assumed that the serial interval was drawn
from a gamma distribution with α=6·0 and β=1·5, and
used Markov Chain Monte-Carlo methods to estimate R
t
.
For the NASA climate data
source see https://power.larc.
nasa.gov/
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We compared both multiplicative and additive dierences
of the new and old R
t
values for days when the proportion
of infections with the B.1.1.7 variant in a region was
greater than 3%. Although data were not available for the
proportion of infections with the B.1.1.7 variant in
January, 2021, we also computed total incidence and R
t
from Oct 1, 2020, to Jan 16, 2021, to see how they changed
during the national lockdown in England.
Role of the funding source
Zoe Global developed the app for data collection. The
funders of the study had no role in the study design, data
collection, data analysis, data interpretation, or writing of
the report.
Results
From March 24 to Dec 27, 2020, 4 327 245 participants
from the UK signed up to use the COVID Symptom Study
app. We excluded users living in Northern Ireland because
of the low number of users who signed up (38 976 users),
as well as 383 352 users without information on sex, and
2 175 979 who had not logged responses in the app between
Sept 28 and Dec 27, 2020, leaving a total of 1 767 914 users.
From Sept 28 to Dec 27, these users collectively recorded
65 613 697 logs in the app. In the same period, 497 989 users
reported a swab test, of whom 55 192 reported a positive
test, and we investigated the symptom reports of the
36 920 of those with a positive test whose region was
known and who reported as healthy on app sign-up. The
table shows the demographic data for the cohort studied.
Between Sept 27 and Dec 31, 2020, 98 170 sequences
were made available by COG-UK, corresponding to
4·
4% of the 2 207 476 cases recorded during this period.
12
16 224 (16·5%) sequences were of variant B.1.1.7. Consider-
ing the mean of the rolling average across December, 2020,
the three regions with the largest propor tion of infections
with the B.1.1.7 variant were the South East, London, and
East of England. The three regions with the lowest
proportion were Wales, the North East and Yorkshire, and
the North West. SGTF data were made available in England
on a weekly basis from Nov 10 to Dec 29, 2020. Of the
700 590 cases reported during this period, 295 404 (42·2%)
caused an SGTF. Examining the COG-UK data from
England in the same period, we found 14 074 (34·6%) of
Overall Tested Tested positive* Signed up healthy, with
reporting around
positive test
Total
Users
1 767 914 497 989 55 192 40 463
Daily reports† 65 613 697 19 154 601 1 514 244 1 497 061
Age, years
Mean (SD) 48·4 (19·3) 46·06 (17·8) 42·1 (16·8) 42·9 (17·0)
≤18
163 112 (9·2%) 40 717 (8·2%) 5468 (9·9%) 3874 (9·6%)
19–64
1 234 259 (69·8%) 381 900 (76·7%) 45 149 (81·8%) 32 878 (81·3%)
≥65
370 543 (21·0%) 72 741 (14·6%) 4367 (7·9%) 3600 (8·9%)
Invalid 5576 (0·3%) 2631 (0·5%) 208 (0·4%) 111 (0·3%)
Sex
Female
1 046 074 (59·2%) 315 875 (63·4%) 34 516 (62·5%) 24 844 (61·4%)
Male 720 562 (40·8%) 181 110 (36·4%) 20 546 (37·2%) 15 545 (38·4%)
Intersex 79 (<0·1%) 21 (<0·1%) 3 (<0·1%) 3 (<0·1%)
Prefer not to say 1199 (0·1%) 983 (0·2%) 127 (0·2%) 71 (0·2%)
Region
South East
342 881 (19·4%) 97 143 (19·5%) 8762 (15·9%) 6555 (16·2%)
East of England
196 063 (11·1%) 57 680 (11·6%) 5373 (9·7%) 4037 (10%)
London
227 004 (12·8%) 81 940 (16·5%) 9733 (17·6%) 7384 (18·2%)
Midlands
198 350 (11·2%) 57 582 (11·6%) 6695 (12·1%) 4756 (11·8%)
North East and Yorkshire
156 999 (8·9%) 42 986 (8·6%) 5292 (9·6%) 3744 (9·3%)
North West
123 201 (7%) 45 156 (9·1%) 6180 (11·2%) 4399 (10·9%)
South West
186 372 (10·5%) 46 780 (9·4%) 3685 (6·7%) 2637 (6·5%)
Scotland
872 63 (4·9%) 13 793 (2·8%) 1589 (2·9%) 1049 (2·6%)
Wales
828 86 (4·7%) 16 471 (3·3%) 3092 (5·6%) 2359 (5·8%)
Not known
165 164 (9·3%) 38 458 (7·7%) 4638 (8·4%) 3543 (8·8%)
Data are n or n (%) unless otherwise specified. Invalid age refers to ages <1 or >100, which were usually caused by incorrect entries (eg, confusing the date of birth field with
age). *There could be more than one test per individual as the overall number contains failed tests and unknown results. †Reports logged between Sept 28 and Dec 27, 2020;
for some analyses we took further reports from an extended period from Sept 14, 2020, to Jan 18, 2021.
Table: Characteristics of COVID Symptom Study app users active between Sept 28 and Dec 27, 2020