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Article https://doi.org/10.1038/s41467-022-34244-2
Variant-specificsymptomsofCOVID-19ina
study of 1,542,510 adults in England
Matthew Whitaker
1,2,10
, Joshua Elliott
3,4,10
,BarbaraBodinier
1,2
,
Wendy Barclay
4
, Helen Ward
3,5,6
, Graham Cooke
3,4,6
,
Christl A. Donnelly
1,5,7
, Marc Chadeau-Hyam
1,2,11
&PaulElliott
1,2,3,6,8,9,11
Infection with SARS-CoV-2 virus is associated with a wide range of symptoms.
The REal-time Assessment of Community Transmission −1(REACT-1) study
monitored the spread and clinical manifestation of SARS-CoV-2 among ran-
dom samples of the population in England from 1 May 2020 to 31 March 2022.
We show changing symptom profiles associated with the different variants
over that period, with lower reporting of loss of sense of smell or taste for
Omicron compared to previous variants, and higher reporting of cold-like and
influenza-like symptoms, controlling for vaccination status. Contrary to the
perception that recent variants have become successively milder, Omicron
BA.2 was associated with reporting more symptoms, with greater disruption to
daily activities, than BA.1. With restrictions lifted and routine testing limited in
many countries, monitoring the changing symptom profiles associated with
SARS-CoV-2 infection and effects on daily activities will become increasingly
important.
A meta-analysis of studies from the first wave of the pandemic iden-
tified 30 symptoms reported in multiple studies1, including common
influenza-like symptoms (cough, fever, myalgia/fatigue, headache,
sputum production), and less common but more specificsymptoms
including change or loss of sense of smell or taste.
Previous community-based studies have assessed the degree to
which symptom data can predict polymerase chain reaction (PCR)
positivity for SARS-CoV-2, and have used variable selection and rank-
ing techniques to identify the most important (set of) symptoms for
case identification2–4. Further studies have indicated that symptom
profiles may differ between variants of SARS-CoV-25–7.
The relationship between symptom profile and cycle threshold
(Ct) value from PCR testing (an established proxy for viral load8–10,
which in turn correlates with infectiousness11,12) has yet to be fully
investigated. Identifying individuals who are more likely to be (i)
infected, and (ii) infectious on the basis of symptomprofile would have
clinical value as governments move away from mass testing pro-
grammes and mandatory isolation measures.
Here, we use regression modelling and variable selection models
in the large community-based REal-time Assessment of Community
Transmission −1 (REACT-1) study that was in the field approximately
monthly from 1 May 2020 to 31 March 2022 to i) describe the symptom
profiles of the main variants of SARS-CoV-2 that have been dominant in
England and worldwide over this period, namely wild-type, Alpha,
Delta and Omicron BA.1 and BA.2, and ii) identify the symptoms that
are most predictive of high viral load, and hence infectiousness, for
each variant.
Results
Descriptive and univariable analysis
The characteristics of our study population are summarised in Fig. 1
and Supplementary Tables 1 and 2. It comprised 1,542,510 adults aged
Received: 1 June 2022
Accepted: 19 October 2022
Check for updates
1
School of Public Health, Imperial College London, London, UK.
2
MRC Centre for Environment and Health, Imperial College London, London, UK.
3
Imperial
College Healthcare NHS Trust, London, UK.
4
Department of Infectious Disease, Imperial College London, London, UK.
5
MRC Centre for Global Infectious
Disease Analysis and Jameel Institute, Imperial College London, London, UK.
6
National Institute for Health Research Imperial Biomedical Research Centre,
London, UK.
7
Department of Statistics, University of Oxford, Oxford, UK.
8
Health Data Research (HDR) UK London at Imperial College, London, UK.
9
UK
Dementia Research Institute at Imperial College, London, UK.
10
These authors contributed equally: Matthew Whitaker, Joshua Elliott.
11
These authors jointly
supervised this work: Marc Chadeau-Hyam, Paul Elliott. e-mail: p.elliott@imperial.ac.uk
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18 and over, including a total of 17,448 swab positive individuals: 2971
(0.4%, 95% Confidence Interval (CI) [0.4,0.4] unweighted prevalence)
for wild type; 2275 (0.6% [0.6,0.7]) for Alpha; 1493 (0.7% [0.6,0.7]) for
Delta and 10,709 (4.4% [4.3,4.5])for Omicron variants (Supplementary
Table 1).
The proportion of swab positive individuals reporting any of
26 symptoms (symptoms listed in Supplementary Table 1) was highest
in those infected with BA.2 (75.9% [74.4,77.2], compared with 70.0%
[68.3,71.6] in those with BA.1, 63.8% [61.3,66.2] in those with Delta,
54.7%, [52.7,56.8] in those with Alpha and 45.0% [43.3,46.8] in those
with wild-type) (Table S2). Background prevalence of symptoms was
also highest during January–March 2022, when Omicron dominated:
21.9%, [21.7,22.0] of all respondents reported one or more symptoms,
compared with 13.5% [13.4,13.5] during the wild-type period (Supple-
mentary Table 1).
Those infected with BA.2 reported an average of 6.0 (95% CI
5.8,6.2) symptoms in the week prior to PCR testing, compared with
2.70 (2.6,2.8), 3.4 (3.2,3.6), 4.6 (4.4,4.9) and 4.6 (4.5,4.8) for wild-type,
Alpha, Delta and BA.1 respectively (Supplementary Table 2). A larger
proportion of people with BA.2 reported that their symptoms had
affected their ability to carry out day-to-day activities ‘alot’(17.6%
[16.3,18.8]) compared with those infected with BA.1 (10.7%[9.6,11.9]) or
Delta (10.5%, [9.1,12.2]) (Supplementary Table 2).
All symptoms were positively associated with swab positivity for
all variants (Fig. 2, Table S3). The odds ratio for swab positivity of ‘any’
vs ‘none’of 26 symptoms was highest for BA.2 (OR = 12.9 [11.9,14.0],
compared with 5.7 [4.8,5.6], 6.0 [5.1,7.1], 9.5 [8.6,10.6] and 9.6 [8.8,10.5]
for wild-type, Alpha, Delta and BA.1, respectively) (Supplementary
Table 3, Fig. 2).
Unlike for wild-type, Alpha, and Delta, where the highest odds
ratios for swab positivity were for loss or change of sense of smell
(ORs 49.7 [44.3,55.7], 37.8 [28.6,50.0] and 73.4 [64.2,83.9],
respectively) or taste (ORs 35.9 [31.9,40.4], 38.9 [29.9,50.6] and
68.1 [59.4,78.0] respectively), for BA.1 and BA.2 influenza-like and
cold-like symptoms were relatively more predictive of swab posi-
tivity, and loss or change of sense of smell or taste relatively less
so. Within BA.1 and BA.2, the highest odds ratio of all symptoms
wasforfever:ORswere18.4[16.5,20.5]forBA.1and30.2
[27.7,33.0] for BA.2, compared with 12.9 [11.1,15.1] and 17.2
[15.1,19.5] respectively for loss or change of sense of smell and 16.0
[13.9,18.5] and 21.3 [18.9,24.0] respectively for loss or change of
sense of taste (Fig. 2, Supplementary Table 3). In a sensitivity
analysis, further adjusting for time since symptom onset atte-
nuated the odds ratios, but the patterns across variants remained
consistent with the main analysis (Supplementary Fig. 7).
A pooled analysis (Methods) reinforced the findings from the
univariable analysis after adjusting for SARS-CoV-2 prevalence and
background symptom prevalence, showing that Alpha and Delta were
associated with increased symptom-specificoddsratiosacrossmost
symptoms, while Omicron BA.1 was associated with lower odds ratios
across most symptoms, and especially for the loss of senseof smell or
taste (Supplementary Fig. 2). Omicron BA.2 was associated with
increased oddsratios vs BA.1,most notably for cold-like symptoms and
chills.
Fig. 1 | Study population flow-chart. Variant prevalence data in bottom panel is from GISAID26.
Article https://doi.org/10.1038/s41467-022-34244-2
Nature Communications | (2022) 13:6856 2
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Multivariable analysis for variable selection
We used Least Absolute Shrinkage and Selection Operator (LASSO)
penalised logistic regression to identify parsimonious symptom sets
selected as jointly and positively predictive of swab positivity for each
variant (Fig. 3, Fig. S3); this method takes into account differences in
symptom cooccurrence by variant (Figs. S4 and S5). Loss or change of
sense of taste, new persistent cough, and fever were selected for each
variant. Notably, cold-like symptoms of runny nose, sore throat,
sneezing and hoarse voice were only selected for Omicron (BA.1
and BA.2).
Omicron (BA.1 and BA.2)
Comparing symptoms for BA.2 vs BA.1 using logistic regression (based
on either model adjustment or matching see “Methods”), infection
with BA.2 was positively associated with chest pain, severe fatigue,
runny nose, muscle aches, sneezing, fever, chills, tiredness, blocked
nose and headache (in both sets of analyses); in unmatched analyses,
infection with BA.2 was further associated with sore eyes, appetite loss
and new persistent cough (Fig. 4).
In a subgroup of 5,598 double- and triple-vaccinated swab-posi-
tive individuals with BA.1 or BA.2, those infected with BA.2 were 54%
more likely to report symptoms that interfered with their ability to
carry out day-to-day activities ‘alot’(OR 1.54 [1.16, 2.06]) vs ‘a little’,
‘not at all’, or not reporting any symptoms, after adjustment for age
group, sex, vaccine count, time since most recent vaccine, prior SARS-
CoV-2 infection, time since symptom onsetand calendar time (Table 1).
In the same models, men were 38% less likely than women to report
symptoms that interfered with their ability to carry out day-to-day
activities ‘alot’(0.62 [0.52,0.73]). Vaccine booster status and time
since vaccination were not associated with ability to carry out daily
activities. In the same subgroup, a log-linear regression of symptom
count found that those infected with BA.2 reported 14% more symp-
toms, on average, than those with BA.1 (OR= 1.14 [1.10,1.19]) after
adjustment for the same covariates as above (Supplementary Table 5).
Ct values. Ct values were lower for BA.2 than BA.1 (Supplementary
Fig. 1). This may reflect the timing of the sampling with respect to the
growth of the variant since more recent infections will tend to have
lower Ct values (see Supplementary Table 2 and Supplementary
Figs. 6 and 7, which show a positive correlation between time-since-
symptom-onset and Ct values, and that mean time since symptom
onset was lower for BA.2 than for BA.1). As expected, symptomatic
individuals had lower Ct values (higher viral loads) than asympto-
matic people. In linear regression models among swab positive
individuals in rounds 17–19 (5 January to 31 March 2022), for each of
the 26 surveyed symptoms, symptom reporting was associated with
a lower Ct value. The lowest adjusted Ct values were for influenza-like
or cold-like symptoms: fever, chills, sore throat, muscle aches, runny
nose, sneezing and headache (Fig. 5), which frequently co-occurred
(Supplementary Fig. 4). With the exception of fever, these symptoms
were also commonly reported as the first symptom among sympto-
matic swab positives (Supplementary Fig. 5, Supplementary Table 2).
Discussion
In this study of more than 1.5 millionadults randomlyselected from the
population in England, we show differences in symptom reporting
Fig. 2 | Comparison of ORs for swab positivity based on presence or absence of
any of 26 symptoms surveyed in N= 1,542,510 participants across five variant-
phases of REACT-1. ORs are derived from logistic regression models with swab
positive (1/0) as theoutcome variable,adjusted for age, sex and vaccination status.
Error bars show 95% confidence intervals. ORs are higher for BA.2 than BA.1 for all
symptoms. Fever and cough have the highest ORs for BA.2 and BA.1, while loss or
change of smell or taste have the highest ORs in all previous variants.
Article https://doi.org/10.1038/s41467-022-34244-2
Nature Communications | (2022) 13:6856 3
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associated with Omicron compared with previous variants, and within
Omicron for BA.2 vs BA.1. This may reflect changes in the underlying
pathophysiology associated with different variants, affecting, for
example, receptor binding, cell entry or host response, against a
background of differing levels of population immunity (both from
natural infection and vaccine-induced)13–15.
We found that loss or change of sense of smell or taste were
less predictive of swab positivity for Omicron than for other var-
iants, and that cold-like symptoms were more predictive for Omi-
cron than for previous variants. Both these findings were consistent
with previous reports5,16,17.Specifically, infections with Omicron
variants are not as strongly associated with anosmia compared with
previous variants. The loss of sense of smell or taste following
infection with earlier variants of SARS-CoV-2 results from the
downregulated expression of olfactory receptors18.Itispossible
that changes in the sequence of viral genes that regulate host
responses in Omicron reduce this effect; detailed transcriptomic
studies in animal models and humans may help to pinpoint the
mechanisms involved.
Comparison of the intrinsic severity of SARS-CoV-2 variants is
complex, owing to changing levels of population immunity due to
prior infection or vaccination15. However, the rapid replacement of
BA.1 by BA.2, and the large number of PCR positives, afforded an
opportunity for comparison of the symptom burden and symptom
severity of the two variants within a population with similar char-
acteristics against a similar background of non-COVID-related illness
and symptoms.
Comparing Omicron BA.2 with BA.1, we found that those with
BA.2 were more likely to be symptomatic, to report a number of
influenza-like and cold-like symptoms, and, in adjusted models, to
report more symptoms, and to report that their symptoms affected
their day-to-day activities ‘alot’. The last two findings were robust to
adjustment for vaccine booster status and time since most recent
vaccine dose and are therefore unlikely to be explained by vaccination
status or waning immunity following vaccination. The effects were
somewhat attenuated by the addition of time since symptom onset
and calendar time, suggesting that the higher symptom burden and
severity of BA.2 (vs BA.1) may to some extent reflect the detection
of swab positivity earlier in the disease course for BA.2; this is con-
sistent with the higher transmissibility of BA.2 in a highly vaccinated
population. Nonetheless, following adjustment, BA.2 was associated
with 54% greater odds of symptoms affecting day-to-day activities ‘a
lot’, and reporting of one additional symptom, on average, com-
pared to BA.1.
While other studies of the BA.2 and BA.1 variants suggested that
they were of similar severity19,20 in terms of case hospitalisation rate or
case fatality rate, the greater symptom burden and severity for
BA.2 shown here may still be associated with substantial disruption to
daily living, and have wider societal and economic impact.
From 1 April 2022 the UK government moved to a policy of ‘living
with COVID’21. With the lifting of restrictions and limited access to
free testing limited, identifying individuals who are particularly likely
to be infectious on the basis of symptoms alone may help reduce
ongoing transmission of SARS-CoV-2. We show that in the Omicron
periodreportingfever,chills,sorethroat,muscleaches,runnynose,
sneezing and headache was associated with the lowest adjusted Ct
values and therefore most likely to be indicative of higher viral load
and increased infectiousness.
Our study has limitations. Response rates varied between 11.7%
and 26.5% for rounds 2–19, so the samples may not be fully repre-
sentative of, or results fully generalisable to, the population. Never-
theless, our random community sampling procedure included
individuals from all of the315 lower tier local authority areas in England
in each round, ensuring wide geographical coverage and socio-
economic and demographic diversity. The symptoms surveyed were
not exhaustive but, while not specific to COVID-19, were all shown to
Fig. 3 | Results of LASSO stability selection proportions with swab positive/
negative as the binary outcome variable and each of 26 symptoms as pre-
dictors, for five SARS-CoV-2 variants in England. Age, sex and, where appro-
priate, vaccination status are forced into the models as unpenalised variables;
regression coefficients for the symptoms are constrained to be positive. The
selection proportion indicates the proportion of LASSO models, trained on sub-
samples of the data, in which each symptom was selected as a predictor.
Article https://doi.org/10.1038/s41467-022-34244-2
Nature Communications | (2022) 13:6856 4
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be predictive of SARS-CoV-2 swab positivity. Our analysis covers a
period of 22 months, during which time background levels of natural
and vaccine-acquired immunity varied substantially, making it difficult
to differentiate the effect of viral mutations from the impact of vac-
cines and prior infection15. As REACT-1 data collection was non-con-
tinuous, we may have captured different stages of epidemic growth
across variants, which may have differentially affected symptom
reporting at different times.
Of those who provided valid swabs and consented to linkage in
rounds 1–19 of REACT-1 (2,191,597 people in total), approximately 3%
(65,915 people) participated in more than one round. On this basis, a
correction factor of 1.015 could therefore be applied to the standard
errorestimates.Wearenotabletodefinitively identify instances of
participation in more than one round among those who did not con-
sent to linkage. However, because the consent-based estimate of the
correction factor is so close to one, we feel confident reporting
uncorrected standard errors and confidence intervals.
In summary, we have detected differences in symptom profiles
reported during nearly 2 years of the COVID-19 epidemic in England,
reflecting the emergence of different variants over that period against
a background of varying immunity from prior infection and vaccina-
tion. Most recently, infection with Omicron is associated with lower
reporting of loss or change of sense of smell and taste, and higher
reporting of cold-like and influenza-like symptoms. Sequence-
confirmed BA.2 was associated with reporting of more symptoms
and greater disruption to daily activity compared with BA.1. As routine
testing becomes more limited in many countries, and as new variants
emerge, understanding the symptom profiles which can identify indi-
viduals with a higher risk of transmission will become increasingly
important.
Methods
Study population
The REACT-1 study has been tracking the prevalence of SARS-CoV-2 in
the general population of England from 1 May 2020 to 31 March 2022.
The study protocol and methodology have been published;2,22 briefly,
every 4–6 weeks, recruitment letters were sent to a random, nationally
representative sample of people aged 5 years and over in England, using
the National Health Service patient register. Participants then obtained
self-administered throat and nasal swabs for SARS-CoV-2 PCR testing
and completed an online or telephone questionnaire which included
questions on demographic variables, behaviour, and recent symptoms.
Questionnaires for each of the 19 completed rounds since May 2020 are
available on the study website (https://www.imperial.ac.uk/medicine/
research-and-impact/groups/react-study/for-researchers/react-1-study-
materials/). Between 95,000 and 175,000 viable swabs and valid
responses were gathered each round, with respondents unaware of
their test result at the time of their response.
Fig. 4 | ORs for infection with BA.2 vs BA.1 among swab-positive respondents.
ORs are derived from (i) logistic regression models with BA.2 vs BA.1 as the binary
outcome variable, and presence or absence of any of 26 symptoms as explanatory
variables, adjusted for age group, sex, round and vaccination status, among
N= 5598 swab-positive individuals with either BA.2 or BA.1 in rounds 17–19 (5 Jan-
uary to 31 March 2022); and (ii) conditionallogistic regression models with BA.2 vs
BA.1 as the outcome variable among 1510 swab-positive individuals with either the
BA.2 or BA.1 variant in rounds17–19, matched 1:1on age (±5 years), sex, vaccination
status and round. In left panel, bars show 95% confidence intervals, and symptoms
are ordered by mean OR across both models. Right panel directly plots the ORs
from thetwo models for comparison.In both analyses,infection with BA.2 (vs BA.1)
is positively associated with chest pain, severe fatigue, runny nose, muscle aches,
sneezing, fever, chills, tiredness, blocked nose and headache; in unmatched ana-
lysis, infection with BA.2 is further associated with sore eyes, appetite loss and new
persistent cough.
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Table 1 | Results from logistic regression of the response to the question “How much, if at all, do the symptoms you have had in the last 7 days reduce your/their ability to
carry out day-to-day activities?”as a function of BA.2 / BA.1 infection, age group, sex, booster vaccine received (y/n), weeks since most recent vaccine, prior COVID-19
(28daysormorebeforetesting),weekssincesymptomonset,andcalendartime (since 1 Jan 2021) among 5,637 double- or triple-vaccinated swab-positive individuals
with either BA.2 or BA.1 infection
Variable Category Crude model Plus age Plus sex Plus boosted Plus weeks since
vaccination
Plus prior
COVID-19
Plus weeks since
symptom onset
Plus calendar
time (weeks)
Omicron variant BA.1 [ref] –– – – – – – –
BA.2 1.86 (1.59,2.18) 1.93 (1.64,2.28) 1.94 (1.64,2.29) 1.97 (1.66,2.33) 1.91 (1.58,2.29) 1.92 (1.59,2.30) 1.70 (1.40,2.06) 1.54 (1.16,2.06)
Age 18–24 [ref] –– – – – – –
25–34 1.22 (0.80,1.84) 1.26 (0.83,1.91) 1.26 (0.83,1.92) 1.26 (0.83,1.91) 1.27 (0.83,1.93) 1.13 (0.73,1.74) 1.13 (0.74,1.75)
35–44 1.64 (1.09,2.46) 1.70 (1.14,2.56) 1.73 (1.15,2.60) 1.71 (1.14,2.57) 1.72 (1.14,2.58) 1.63 (1.07,2.49) 1.65 (1.08,2.51)
45–54 2.07 (1.39,3.10) 2.19 (1.47,3.29) 2.25 (1.49,3.38) 2.21 (1.47,3.33) 2.20 (1.46,3.32) 2.09 (1.37,3.19) 2.12 (1.39,3.25)
55–64 2.12 (1.42,3.16) 2.30 (1.54,3.43) 2.36 (1.57,3.55) 2.31 (1.54,3.48) 2.32 (1.54,3.48) 2.24 (1.47,3.41) 2.28 (1.49,3.49)
65–74 1.24 (0.81,1.88) 1.39 (0.91,2.11) 1.43 (0.93,2.19) 1.39 (0.90,2.14) 1.37 (0.89,2.11) 1.33 (0.85,2.08) 1.36 (0.86,2.13)
Sex Female [ref] ––– – – –
Male 0.57 (0.48,0.67) 0.57 (0.48,0.68) 0.57 (0.48,0.68) 0.57 (0.48,0.68) 0.62 (0.52,0.73) 0.62 (0.52,0.73)
Boosted (Yes) No [ref] –– – – –
Yes 0.90 (0.71,1.15) 0.97 (0.72,1.31) 0.96 (0.71,1.29) 0.92 (0.67,1.27) 0.88 (0.62,1.24)
Weeks since last vaccination 1.00 (0.99,1.01) 1.00 (0.99,1.01) 1.01 (0.99,1.02) 1.00 (0.99,1.02)
Prior COVID-19
(28+ days ago)
No [ref] –– –
Yes 0.64 (0.47,0.87) 0.92 (0.65,1.30) 0.92 (0.65,1.29)
Weeks since symptom onset 0.81 (0.66,0.98) 0.81 (0.67,0.98)
Calendar time (weeks
since 1 Jan)
1.02 (0.98,1.05)
Each column shows the addition of one covariate to the model. Odds ratios and 95% confidence intervalsare shown. BA.2 infection (vs BA.1) is associated with increased risk of reduced ability to carry out day-to-day activities.This effectisrobusttoadjustment.
Vaccine booster status was not associated wi th a change in ability to carry out daily activiti es.
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Participants were asked whether they experienced any of a list of
26 potential COVID-19 symptoms in the week prior to their test.
These included loss or change of sense of smell or taste, respiratory/
cardiac symptoms (new persistent cough, chest pain, tight chest,
shortness of breath), cold-like symptoms (runny nose, blocked nose,
sneezing, sore throat, hoarse voice, sore eyes), influenza-like symp-
toms (fever, chills, muscle aches, headache), gastrointestinal symp-
toms (nausea/vomiting, abdominal pain/belly ache, diarrhoea,
appetite loss), fatigue-related symptoms (tiredness, severe fatigue,
difficulty sleeping), and others (dizziness, heavy arms or legs,
numbness/tingling).
We split data from 15 rounds of REACT-1 between 19 June 2020
and 31 March 2022 into distinct phases that correspond with the
dominance of different SARS-CoV-2 variants in England: rounds
2–7 (at approximately monthly intervals between 19 June and 3
December 2020), when wild-type was dominant; rounds 8–10
(between 6 January and 29 March 2021), when Alpha (B.1.1.7) was
dominant; rounds 13–15 (between 24 June and 5 November 2021),
when Delta (B.1.617.2) was dominant; and rounds 17–19 (between 5
January and 31 March 2022), when Omicron (B.1.1.529) was domi-
nant. In rounds 17–19 we use sequencing data to identify those
participants who were infected with BA.1 or BA.2. Round 1 is
excluded because the symptom questions asked were not con-
sistent with subsequent rounds. Rounds 11, 12 and 16 are excluded
from analysis because they occurred at times when two variants
were competing for dominance in the population23.
Adults aged 18 years and over were included in the analysis. A total
of 266,847 participants were excluded because of missing symptom
data (see supplementarymethods for moredetails on data exclusions),
and 38 were excluded because of missing age orsex data resulting in a
final study population, after exclusions, of 1,542,510 participants.
Statistical analyses
We used univariable logistic regression models to estimate the risk
of PCR swab-positivity for each variant conditional on experien-
cing each of the 26 symptoms. Models were adjusted for age
group, sex, and self-reported vaccination status (coded as the
number of vaccines received). Odds ratios and 95% confidence
intervals are reported for each symptom and each variant. We also
conducted a pooled analysis in which we tested the interactions
between variants and each symptom in relation to PCR positivity,
while additionally adjusting for calendar time, to assess the effect
of differing SARS-CoV-2 prevalence and background symptom
prevalence on the estimated odds ratios.
Variable selection models were trained on 70% of the data set,
with 30% held back for model performance evaluation (see Supple-
mentary Methods). We used stability selection applied to least abso-
lute shrinkage and selection operator (LASSO) penalised logistic
regression, with swab positivity as the binary outcome variable, and
the 26 symptoms as predictors. To adjust for age, sex and vaccination
status, these were included as unpenalised variables. The regression
coefficients for selected symptoms were constrained to non-negative
Loss or change of sense of smell
Shortness of breath
Severe fatigue
Loss or change of sense of taste
Abdominal pain / belly ache
Diarrhoea
Chest pain
Tight chest
Tiredness
Dizziness
Nausea/vomiting
Sore eyes
Difficulty sleeping
Numbness/tingling
Appetite loss
Blocked nose
Heavy arms/legs
Hoarse voice
New persistent cough
Headache
Sneezing
Runny nose
Muscle aches
Any of 26 symptoms
Sore throat
Chills
Fever
3 2 1 0 1
Difference in Ct value (adjusted)
Fig. 5 | Results of linear regression models with N-gene Ct values as the out-
come variable and symptoms as individual predictors, adjusted for age, sex
and, where appropriate, vaccination status, among N = 10,709 swab-positive
respondents in rounds 17–19 (5 January to 31 March 2022). Error bars show 95%
confidence intervals. Fever, chills, and sore throat are the symptoms with the
strongest negative association with Ct value, each associatedwith approximately a
tenfold increase in viral load.
Article https://doi.org/10.1038/s41467-022-34244-2
Nature Communications | (2022) 13:6856 7
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values. LASSO models were fit on 1000 random 50% subsamples of the
70% training data. The proportion of models in which each symptom
was selected is taken as a measure of variable importance. The
threshold in selection proportion for final variable selection was cali-
brated in conjunction with the LASSO penalty parameter using an
internal stability score24.
BA.2 vs BA.1. Omicron BA.2 and BA.1 lineages were determined using
viral genome sequencing on swab-positive swabs from rounds 17–19.
We compared the symptom profiles among (BA.2 or BA.1) swab-
positive individuals using logistic regression with BA.2 vs BA.1 as the
binary outcome variable and each of the 26 symptoms as explanatory
variables, adjusted on age group, sex, vaccination status and round. As
a sensitivity analysis, we 1:1 matched swab-positive participants with
BA.2 or BA.1 on age group (±5 years), sex, vaccination status and round
in rounds 17–19, and conducted conditional logistic regression with
BA.2 vs BA.1 as the binary outcome variable. We also used log-linear
regression to compare symptom burden, in terms of number of
symptoms experienced over the disease course, in double- and triple-
vaccinated individuals with Omicron BA.2 and BA.1 (Supplementary
Methods).
Severity of symptoms. To assess whether there are differences in
symptom severity between BA.2 and BA.1 independent of vaccination
history we took a subset of swab-positive individuals with sequence-
confirmed BA.2 or BA.1 who had received second or third vaccines at
least two weeks before their PCR test. In this group, we used logistic
regression to compare the risk of reporting symptoms that affected
their daily activities ‘alot’vs ‘a little’or ‘not at all’in people infected
with BA.2 vs BA.1. We adjusted for age, sex, vaccine boosted (y/n), days
since most recentvaccination, prior COVID-19 (28 days or more before
test date), time since symptom onset, and calendar time (to account
for seasonal effects). Odds ratios were reported for sequential models,
with additional covariates added incrementally in the order described.
We also used the same subset of individuals to model symptom count
using multivariable log-linear regression models, again adding cov-
ariates sequentially and reporting odds ratios.
Ct values. Finally, we investigated the relationship between N-gene
Ct value and symptom profile among swab positive individuals in
rounds 17–19 (>95% Omicron), using linear regression models with Ct
value as the outcome variable and each symptom separately as the
independent variable. We also compared Ct values between swab-
positive individuals with BA.2 or BA.1 using an unpaired Wilcoxon
test, and compared Ct values in those excluded from analysis
because of missing symptom data. Finally, we used linear regression
to associate Ct values with time since symptom onset, in individuals
with BA.2 or BA.1.
Sensitivity analyses. To assess possible biases introduced by non-
continuous sampling, which might capture different stages of symp-
tom onset for different variants, we (i) investigated the distributions of
‘days since symptom onset’for different variants, (ii) investigated the
relationship between symptom burden, in terms of number of symp-
toms reported, and days since symptom onset, and (iii) repeated the
main analysis, further adjusting for time since symptom onset in the
models.
Data collection and software. All data collection was captured with
Questback (Sprint 2020 Installation). Data were analysed using R ver-
sion 4.0.525.
Reporting summary
Further information on research design is available in the Nature
Research Reporting Summary linked to this article.
Data availability
The original datasets generated or analysed, or both, during this study
are not publicly available because of governance restrictions and the
identifiable nature of the data. Requests for access to raw data should
be addressed to the corresponding authors and will be answered
within 12 weeks. Summary statistics, descriptive tables, and code from
the current REACT-1study are available at https://github.com/mrc-ide/
reactidd/tree/master/inst/extdata/variant_symptom_profiling_paper.
REACT-1 study materials are available for each round at https://www.
imperial.ac.uk/medicine/research-and-impact/groups/react-study/for-
researchers/react-1-study-materials/ Sequence read data are available
without restriction from the European Nucleotide Archive at https://
www.ebi.ac.uk/ena/browser/view/PRJEB37886, and consensus gen-
ome sequences are available from the Global initiative on sharing all
influenza data (GISAID)26. GISAID accession numbers for all sequences
in the REACT1 study have been published in supplementary data file 1
of Eales et al.27.
Code availability
Scripts for this paper are available at https://github.com/mrc-ide/
reactidd/tree/master/inst/extdata/variant_symptom_profiling_paper.
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Acknowledgements
The study was funded by the Department of Health and Social Care in
England. The funders had no role in the design and conduct of the
study; collection, management, analysis, and interpretation of the
data; and preparation, review, or approval of this manuscript. MW is
supported by grants from NIHR and UK Research and Innovation
(UKRI): REACT GE (MR/V030841/1) and REACT Long COVID (REACT-LC)
(COV-LT-0040). JE is an NIHR academic clinical fellow in infectious
diseases. HW acknowledges support from an NIHR Senior Investigator
Award, the Wellcome Trust (205456/Z/16/Z), and the NIHR Applied
Research Collaboration (ARC) North West London. GC is supported by
an NIHR Professorship. CAD acknowledges support from the MRC
Centre for Global Infectious Disease Analysis, the NIHR Health Pro-
tection Research Unit in Emerging and Zoonotic Infections and the
NIHR-funded Vaccine Efficacy Evaluation for Priority Emerging Dis-
eases (PR-OD-1017-20007). MC-H and BB acknowledge support from
Cancer Research UK, Population Research Committee Project grant
‘Mechanomics’(grant No 22184 to MC-H). MC-H acknowledges
support from the H2020-EXPANSE (Horizon 2020 grant No 874627)
and H2020-LongITools (Horizon 2020 grant No 874739). PE is Director
of the Medical Research Council (MRC) Centre for Environment and
Health (MR/L01341X/1, MR/S019669/1). PE acknowledges support
from Health Data Research UK (HDR UK); the National Institute for
Health Research (NIHR) Imperial Biomedical Research Centre; NIHR
Health Protection Research Units in Chemical and Radiation Threats
and Hazards, and Environmental Exposures and Health; the British
Heart Foundation Centre for Research Excellence at Imperial College
London (RE/18/4/34215); and the UK Dementia Research Institute at
Imperial College London (MC_PC_17114).
Author contributions
M.W.: conceptualisation, methodology, formal analysis, investigation, data
curation, writing—original draft, writing—review and editing, visualisation;
J.E.: conceptualisation, methodology, investigation, writing—original draft,
writing—review and editing; B.B.: conceptualisation, methodology, soft-
ware, writing—original draft; W.B.: conceptualisation, methodology,
investigation, writing—original draft; H.W.: conceptualisation, methodol-
ogy, writing—original draft, writing—review and editing, supervision,
funding acquisition; G.C.: conceptualisation, methodology, writing—ori-
ginal draft, writing—review and editing, investigation, supervision, funding
acquisition; C.A.D.: conceptualisation, methodology, writing—original
draft, writing—review and editing, investigation, supervision; M.C.-H.:
conceptualisation, methodology, investigation, writing—original draft,
writing—review and editing, supervision; P.E.: conceptualisation, metho-
dology, investigation, writing—original draft, writing—review and editing,
supervision, funding acquisition.
Ethics
We obtained research ethics approval from the South Central-Berkshire
B Research Ethics Committee (IRAS ID: 283787). Notification of favour-
able opinion and brief summary of the protocol are available here:
https://www.hra.nhs.uk/planning-and-improving-research/application-
summaries/research-summaries/react1-covid-19-uph/. Participants pro-
vided informed consent for their data to be used and, separately, indi-
cated whether they were willing for their data to be linked to their NHS
records.
Public involvement
A Public Advisory Panel provides input into the design, conduct, and
dissemination of the REACT research program.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains
supplementary material available at
https://doi.org/10.1038/s41467-022-34244-2.
Correspondence and requests for materials should be addressed to
Paul Elliott.
Peer review information Nature Communications thanks Vahe Nafilyan
and the other, anonymous, reviewer(s) for their contribution to the peer
review of this work. Peer reviewer reports are available.
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