Jarvis et al. BMC Medicine(2020) 18:124
https://doi.org/10.1186/s12916-020-01597-8

RESEARCH ARTICLE

Open Access

Quantifying the impact of physical distancemeasures on the transmission of COVID-19
in the UK
Christopher I. Jarvis1*†, Kevin Van Zandvoort1†, Amy Gimma1, Kiesha Prem1, CMMID COVID-19 working group,
Petra Klepac1, G. James Rubin2 and W. John Edmunds1

Abstract
Background: To mitigate and slow the spread of COVID-19, many countries have adopted unprecedented physicaldistancing policies, including the UK. We evaluate whether these measures might be sufficient to control theepidemic by estimating their impact on the reproduction number (R0, the average number of secondary cases
generated per case).
Methods: We asked a representative sample of UK adults about their contact patterns on the previous day. The
questionnaire was conducted online via email recruitment and documents the age and location of contacts and ameasure of their intimacy (whether physical contact was made or not). In addition, we asked about adherence todifferent physical distancing measures. The first surveys were sent on Tuesday, 24 March, 1 day after a “lockdown”
was implemented across the UK. We compared measured contact patterns during the “lockdown” to patterns ofsocial contact made during a non-epidemic period. By comparing these, we estimated the change in reproduction
number as a consequence of the physical distancing measures imposed. We used a meta-analysis of publishedestimates to inform our estimates of the reproduction number before interventions were put in place.
Results: We found a 74% reduction in the average daily number of contacts observed per participant (from 10.8 to2.8). This would be sufficient to reduce R0 from 2.6 prior to lockdown to 0.62 (95% confidence interval [CI] 0.37–0.89) after the lockdown, based on all types of contact and 0.37 (95% CI = 0.22–0.53) for physical (skin to skin)contacts only.
Conclusions: The physical distancing measures adopted by the UK public have substantially reduced contact levels
and will likely lead to a substantial impact and a decline in cases in the coming weeks. However, this projected
decline in incidence will not occur immediately as there are significant delays between infection, the onset ofsymptomatic disease, and hospitalisation, as well as further delays to these events being reported. Tracking
behavioural change can give a more rapid assessment of the impact of physical distancing measures than routine
epidemiological surveillance.
Keywords: COVID-19, Contact survey, Pandemic, Disease outbreak, Reproduction number, nCov

* Correspondence: Christopher.Jarvis@lshtm.ac.uk†
Christopher I. Jarvis and Kevin van Zandvoort contributed equally to thiswork.
1
Centre for Mathematical Modelling of Infectious Diseases, Department of
Infectious Disease Epidemiology, London School of Hygiene & Tropical
Medicine, Keppel Street, London WC1E 7HT, UK
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Jarvis et al. BMC Medicine(2020) 18:124

Background
Over 1.8 million cases and over 110,000 deaths fromCOVID-19 have been recorded worldwide as of 13 April
2020 [1]. A major route of transmission of SARS-CoV2
is via droplet spread which requires close contact [2]. In
an attempt to mitigate the COVID-19 pandemic, many
countries have adopted unprecedented physical distancing policies [3]. On March 23, with just over 6000 confirmed cases, the UK Government implemented strict
physical distancing measures instructing individuals tostay at home and avoid leaving their house except for essential work, to take one form of exercise a day, and tobuy essential items such as food and medicines. This
followed the closure of sporting events, schools, restaurants, bars, gyms, and other leisure or hospitality-related
businesses the previous week [4] and an increase in social distancing among the population that had been taking place for several days before the announcement [5].
Physical distancing interventions attempt to reduce
contacts relevant to infectious disease spread between
individuals. Multiple surveys have been instigated on the
uptake of different physical distancing measures during
this current pandemic, but these have not explicitly measured contacts between people [6–8]. To make accurate
predictions on the impact of these measures, quantitative data on relevant contact patterns is required [9–12].
Many governments have adopted physical distancing
measures to mitigate the impact of the COVID-19 pandemic. However, it is unclear to what extent these measures reduce the number of contacts and therefore
transmission. Only one previous survey—conducted in
two Chinese cities, Wuhan and Shanghai, in February
2020—quantified the impact of these measures on individuals’ contact patterns during the COVID-19 pandemic [13].
Changes in human contact behaviour drive respiratory
infection rates. Understanding these changes at different
stages of the COVID-19 pandemic allows us to rapidly
quantify the impact of physical distancing measures on
the transmission of pathogens. In this paper, we describe
a survey of contact patterns and compliance with physical distance measures and present results from a sample
of adults in the UK. We evaluate whether these measures might be sufficient to control the epidemic by estimating their impact on the reproduction number (theaverage number of secondary cases generated per case).
Methods
Ethics statement

Participation in this opt-in study was voluntary, and all
analyses were carried out on anonymised data. The
study was approved by the ethics committee of the
London School of Hygiene & Tropical Medicine reference number 21795.

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Survey methodology

We commissioned the market research company Ipsos
to conduct a survey of UK adults (referred to here as the
CoMix survey). Adults (≥ 18 years) were recruited into
the survey by sending email invitations to existing members of their online panel. Representativeness of the general UK population was ensured by setting quotas onage, gender, geographical location, and socioeconomic
status. This cohort of individuals will be requested to answer the survey every 2 weeks for a total of 16 weeks totrack changes in their self-reported behaviour. The firstsurveys were sent on Tuesday, 24 March, 1 day after alockdown was announced for the UK.
Participants were asked about their attitudes towards
COVID-19 and the effect of physical distancing interventions, whether they or any of their household members experienced any recent symptoms, whether they
were tested for COVID-19, whether they had had any
contact with known COVID-19 cases, and whether they
were affected by physical distancing measures.
Participants reported (i) if any person in their household were advised to quarantine, isolate, or limit time intheir workplace or educational facility in the preceding
7 days due to COVID-19 and (ii) if they heeded the advice and isolated, quarantined, or stayed away from theirworkplace or educational facility. In the survey, we defined quarantine as limiting contacts and staying athome, with restricted allowance for movement outside
the home after a potential exposure with a COVID-19
case. We defined isolation as completely separating from
uninfected contacts, including household members, either in the home or in a health facility. To assess the impact of advice and policy changes regarding physicaldistancing, we asked participants to indicate if they hadplanned to participate in a set of events in the preceding
week. For each event type, they reported (i) whether they
proceeded with their plan, or (ii) if it was cancelled orthey decided not to go, and (iii) the frequency of theevent type in the previous 7 days. Additional questions
were asked about preventive behaviours, such as handwashing or wearing masks, and about the use of public
transport in the previous 7 days.
In addition, we asked participants to record all directcontacts made between 5 am the day preceding the survey and 5 am the day of the survey. A direct contact was
defined as anyone who was met in person and withwhom at least a few words were exchanged, or anyone
with whom the participants had any sort of skin-to-skincontact. We were unable to ask parents to provide contact information for their children due to lack of ethicalapproval; however, participants were able to list contacts
who were under 18.
For every recorded contact, participants documented
the age and gender of the contact, relationship to theJarvis et al. BMC Medicine(2020) 18:124

contact, the frequency with which they usually contact thisperson, whether contact was physical (skin to skin) or not,and the setting where the contact occurred (e.g. at home,
work, school, or whilst undertaking leisure activities), including whether contact occurred in- or outside an
enclosed building. Questions on social contacts were consistent with those from the UK arm of the POLYMOD
survey [14], which was used as the baseline pre-pandemic
comparison dataset. Details on survey methodology, thestudy protocol, and a copy of the questionnaire used areprovided in Additional files 1 and 2.
Statistical analysis

R version 3.6.3 was used for all analyses; the code anddata are available on github (see the “Availability of data
and materials” section) [15–17].
We grouped study participants and contacts into thefollowing age bands: 18–29, 30–39, 40–49, 50–59, 60–
69, and 70+. Age, gender, and locations of participants
were compared to the 2018 mid-year estimates provided
by the UK Office of National Statistics (ONS) to assess
the representativeness of the study sample [18]. We descriptively analysed answers related to symptoms, attitudes, exposure to physical distancing measures, andindividual preventative measures. We present the number and percentage or mean and standard deviation
where appropriate (Table 3).
We calculated the average number of social contacts
per person per day overall, and stratified by age category,sex, household size, location of contact, type of contact,and day of the week. We then compared the mean total
number of daily contacts by age group to POLYMOD
stratified by contact location.
We calculated social contact matrices for the agespecific daily frequency of direct social contacts, adjusting for the age distribution in the study population andreciprocity of contacts, using the socialmixr package inR [19].
As children (< 18 years) were not included as survey
participants, we imputed contacts for younger age
groups (child-child and child-adult contacts) using the
POLYMOD UK data. Specifically, for those child contact
groups that were missing, we used a scaled version of
the POLYMOD social contact matrix. Following previous methods developed by Klepac et al. [20], as the scaling factor, we took the ratio of the dominant eigenvaluesof the POLYMOD and CoMix matrices, for all age
groups present in both studies, stratified by setting. Furthermore, to reflect school closures during the collectionof our survey, we removed school contacts from the
POLYMOD data from our analysis.
The basic reproduction number, or R0, is the average
number of secondary infections arising from a typicalsingle infection in a completely susceptible population

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and can be estimated as the dominant eigenvalue of thenext generation matrix [21]. The exact form of the next
generation matrix is model dependent. For respiratory
infections, such as SARS-CoV-2 (the pathogen causing
COVID-19), this is usually a function of the age-specificnumber of daily contacts, the probability that a single
contact leads to transmission, and the total duration of
infectiousness. Therefore, R0 is proportional to the dominant eigenvalue of the contact matrix [19].
We assumed that contact patterns prior to physicaldistancing were similar to those observed in the POLYMOD data and that the duration of infectiousness andthe probability that a single contact leads to transmission did not change during the study period. We also assume that all age groups contribute equally totransmission. Under these assumptions, the relative reduction in R0 is equivalent to the reduction in the dominant eigenvalue of the contact matrices. By multiplying
the value of R0 prior to the interventions by the ratio ofthe dominant eigenvalues from the POLYMOD and
CoMix contact matrices, we were able to calculate R0
under the physical distancing interventions. Prior to interventions, we assumed R0 followed a normal distribution with mean 2.6 and standard deviation of 0.54 basedon a meta-analysis of the literature presented in Additional file 3 [22–34].
To assess uncertainty, we repeated the age imputation
process by taking 10,000 bootstrapped samples fromboth POLYMOD and CoMix matrices. For every bootstrap sample, we calculated the ratio between the dominant eigenvalues for the sampled POLYMOD and
CoMix matrices. This sampling provided a distributionof relative change in R0 from the contact patterns observed in POLYMOD and CoMix. Subsequently, we
scaled the initial distribution of R0 with the distributionof bootstrap samples to estimate R0 under physical distancing interventions.
Recent results of the BBC Pandemic study [20] suggested a decrease of nearly 50% in the average numberof contacts made by teenagers (13–18 years) comparedwith the POLYMOD data. We assessed the sensitivity of
our results to a potential reduction in contacts over timeby taking a conservative reduction of 50% between 5 and18 year olds in the POLYMOD study and repeating ourapproach to estimate the reduction in R0.

Results
Participant characteristics

We surveyed 1356 UK participants who recorded 3849
contacts. The average age of participants was 47.2 years(standard deviation (SD) = 15, max = 86), and 45% (608/1356) were female (see Table 1). The average household
size was 2.8 (SD = 1.4, max = 10). Data were collected between Tuesday 24 and Friday 27 March 2020 inclusive.

Jarvis et al. BMC Medicine(2020) 18:124

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Table 1 Participant characteristics in the CoMix survey, and comparison with 2018 mid-year UK population estimates provided by
the Office of National Statistics. The CoMix survey does not include children under the age of 18
Number of participants (%)*

UK ONS mid-year Estimate

198 (16.0%)

23.2%

Location (N = 1240)
North of England
Midlands and East of England

328 (26.5%)

25.4%

London205 (16.5%)

13.4%

South of England

302 (24.4%)

22.2%

Wales

54 (4.4%)

4.7%

Scotland121 (9.8%)

8.2%

Northern Ireland

32 (2.6%)

2.8%

Missing

116

–

0–9

0

–

10–19

28 (2.1%)

–

20–29

185 (13.6%)

17.1%

30–39

275 (20.3%)

17.4%

40–49

249 (18.4%)

16.7%

50–59

233 (17.2%)

17.6%

Age group (N = 1356)**

60–69

280 (20.7%)

13.9%

70+

106 (7.8%)

17.3%

Missing

0

–

Males

748 (55.2%)

49.4%

Females608 (44.8%)

50.6%

Missing0–

Gender (N = 1356)*Within-group percentages**There are no individuals aged less than 18 in the survey participants; therefore, we only compare the percentages of age groups that are fully observed in the
study from the ONS mid-year estimates

Participants were recruited from across the UK. The
sample included participants from London (16.5%),
North of England (16.0%), Midlands and East of England(26.5%), South of England (24.4%), Wales (4.4%),
Scotland (9.8%), and Northern Ireland (2.6%), whilst 116
participants did not report their region (Table 1). Further details of participant demographics and the average
number of contacts stratified by age, gender, household
size, and location are presented in Table 2. Compared tothe mid-year ONS population estimates taken from2018, individuals over 70 years and individuals between
the ages of 20–29 years were undersampled.
Thirteen participants reported having been tested for
COVID-19 with seven testing positive, and two participants still waiting for their results. Forty-one participants
stated they had been in contact with a known COVID19 case. In terms of perceived risk, 26.4% (359/1356)
thought that it was likely that they would develop coronavirus and 48.0% (652/1356) agreed or strongly agreed
that COVID-19 would be a serious disease for them ifthey acquired the infection.

Impact of physical distancing measures

Participants reported data on a total of 3824 household
members, including themselves, of whom 508 (13.2%) had
been asked to quarantine and 826 (21.6%) had been asked toisolate. Nearly a quarter (921; 24.1%) of household members
lived in a house with someone who had at least one symptom of fever, aches, shortness of breath, or cough. Roughly
50% of the 2122 employed individuals had either been asked
to limit their time at work, had their work closed, and/or did
not visit their work in the preceding 7 days (Table 3). Of
those household members who attend educational establishments, 67.2% (818/1217) had their institution closed with63.3% not visiting during the previous 7 days.
There were clear suggestions that physical distancing
in the previous week had impacted planned activities forsurvey participants with 51.3% of participants that
intended to go to a concert being unable as the event
was cancelled, 40.6% intending to go to the cinema wereunable as the cinema was closed, and 32.5% of participants having to cancel plans to visit a pub (Table 3).
Contrastingly, only a small percentage of participantsJarvis et al. BMC Medicine(2020) 18:124

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Table 2 Number of recorded contacts per participant per day stratified by age, gender, household size, and day of the week
Category

Value

Number of participants

CoMix reported contacts, mean (IQR)

POLYMOD reported contacts, mean (IQR)

Overall

Overall1356

2.8 (1, 4)

10.8 (6, 14)

18–29

213

3.0 (1, 4)

12.1 (7, 16)

30–39

275

3.1 (1, 4)

11.3 (6, 15)

40–49

249

3.1 (1, 4)

12.0 (6, 17)

50–59

233

3.0 (1, 4)

9.5 (5, 13)

60–69

280

2.5 (1, 3)

9.0 (5, 12)

70+

106

2.0 (1, 3)

7.6 (4, 12)

Female608

2.9 (1, 4)

11.3 (6, 15)

Male

748

2.8 (1, 4)

10.2 (5, 13)1203

1.6 (1, 2)

7.4 (3, 11)2431

2.3 (1, 3)

10.1 (5, 13)3363

2.7 (2, 3)

11.2 (6, 15)4207

4 (3, 4)12.1 (7, 16)

4+

152

4.7 (4, 6)

14.2 (9, 17)

24 March 2020

Tuesday178

3.0 (1, 43)–

25 March

Wednesday1014

2.8 (1, 4)–

26 MarchThursday162

2.9 (1, 3)–

27 MarchFriday2

5.0 (5, 5)–

Gender of participant

Household size

Date

(2.5%) who intended to go to the supermarket were unable due to COVID-19.
Contact patterns

The mean number of physical (skin to skin) and nonphysical contacts per person measured during this study

was 2.8 (interquartile range [IQR] = 1–4) which was 74%
lower than was measured in POLYMOD (10.8; 6–14).
The reduction in mean contacts between POLYMOD
and CoMix was consistent across age, gender, andhousehold size (Table 2). The respective social contact
matrices (including physical and non-physical contacts)

Table 3 Indicators of adherence with public health interventions and behaviour changes for all household members reported byparticipants
MeasureQuarantine (N = 3824)
Isolation (N = 3824)
Setting

Asked toHave been in

At least with COVID-19 symptom

508 (13.2%)

778 (20.3%)

Living in a household

921 (24.1%)

826 (21.6%)

1264 (33.1%)

People462 (12.1%)

Asked to limit time

Reported as closed

Did not visit

Work (N = 2122)

1006 (47.4%)

996 (46.9%)

1149 (54.1%)

School or university (N = 1217)

651 (47.4%)

818 (67.2%)

771 (63.3%)

Event

Intended to visit

VisitedCancelled

Chose not to visit

Concert111

6 (5.4%)57 (51.3%)

20 (18.1%)

Cinema133

11 (8.3%)

54 (40.6%)

43 (32.3%)

Sporting event
Participant105

14 (13.3%)

46 (43.8%)

33 (31.4%)

Attendee100

9 (9.0%)54 (54.0%)

20 (20.0%)

Restaurant271

28 (10.3%)

118 (43.5%)

100 (36.9%)

Religious event105

14 (13.3%)

68 (64.7%)

33 (31.4%)

Pub

366

105 (28.6%)

119 (32.5%)

24 (6.6%)

Supermarket

1127

967 (85.8%)

28 (2.5%)

112 (10.0%)

Jarvis et al. BMC Medicine(2020) 18:124

also reflected a much lower number of mean contacts
across the age strata as presented in Fig. 1.
The majority of contacts (57.6%) occurred at home,
contrasting with 33.7% reported in the POLYMOD survey. Figure 2 displays the average number of contacts
across age groups for all, physical, home, work, school,
and other contacts. The matrices are consistent with the
majority of contacts being in the home, with work, andother contributing very little to the overall number ofcontacts.

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Estimated the basic reproduction number of COVID-19
under physical distancing

CI = 0.21–0.52). The average pre- to post-intervention ratioin R0 was 0.24 (min = 0.21, max = 0.27) for all contacts and0.14 (min = 0.12, max = 0.17) for physical (skin to skin) contacts only. Based on these values, the physical distancing
measures would have reduced the mean estimate of R0 to
below one even if the initial R0 had been as high as 3.6 assuming all contacts are equally risky or 4.2 assuming only
physical contacts result in transmission.
In a sensitivity analysis, reducing contacts made by 5–17
year olds by 50% made little difference to the results. Under
this assumption, the estimated value of R0 for all contacts
would be 0.69 (95% CI 0.42–0.98) and 0.37 (95% CI 0.22–0.53) if physical contacts alone result in transmission.

We estimated the current R0 under physical distancing
measures to be 0.62 (95% confidence interval [CI] 0.37–0.89) based on all types of contact (Fig. 1). Based on
physical contacts only, we estimated R0 to be 0.37 (95%

Discussion
The measures introduced by the UK Government appear
to have high levels of uptake among participants andFig. 1 Comparison of CoMix and POLYMOD contact matrices and estimated reduction in reproduction number due to physical distancing for all
and physical (skin to skin) contacts separately. a Social contact matrices showing the average total number of daily reported contacts made byparticipants in different age groups with individuals in other age groups, with results shown for all contacts reported in the CoMix and POLYMOD
data. Participants’ contacts in CoMix for age groups 0–4 and 5–17 are imputed using the POLYMOD data. b The estimated value of R0 at the timeof the survey, assuming values of R0 ~ Norm (2.6, SD = 0.54) prior to physical distancing reducing all contacts for all and physical (skin toskin) contacts

Jarvis et al. BMC Medicine(2020) 18:124

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Fig. 2 Contact matrices for all reported contacts made in different settings, comparing CoMix to POLYMOD

have resulted in very large (74%) reductions in the total
number of contacts. If similar changes are observed
across the UK population, we would expect the basic
reproduction number to now be below 1 (0.62; 95% CI
0.37–0.89) and that these physical distancing measures
will lead to a decline in cases in the coming weeks. However, this projected decline in incidence will not result in
an immediate decline in reported cases, as there are significant delays between infection and the onset of symptomatic disease and hospitalisation, as well as further
delays to these events being reported. Hence, routine
surveillance data are unlikely to show a decline in cases
for some time. However, by directly measuring individuals’ contact patterns and estimating the corresponding
basic reproduction number, we are able to rapidly quantify the impact of physical distancing on transmission.
The total number of daily contacts (mean of 2.8 perperson) was significantly reduced compared to patterns
previously estimated in the POLYMOD study (10.7; excluding children < 18 years old) and more recently by
the BBC Pandemic study (10.5; excluding under 13 yearolds) [20]. The observed reduction appears to be unlikely
due to chance given the large difference in average contacts and is consistent with a recent study conducted in
Wuhan, China, that estimated a reduction in the average
number contacts per day from 14.6 prior to the outbreak
to 2.0 under physical distancing interventions [13].
Whilst we are unaware of any directly comparable datafrom the UK, our findings are certainly consistent withother reports from the UK of a dramatic reduction in social contacts, with, for example, only half of respondents
in one survey reporting having left the house at all in
the past 24 h [5].
There are several limitations to this survey. Asking individuals to report their contacts from the day before
may result in recall bias. Moreover, individuals who are
adhering to physical distancing measures may have been
more likely to respond to this survey, potentially resulting in selection bias and in an overestimate of the impact of these measures. The POLYMOD survey usedpaper-based diaries whereas CoMix utilises an online
form, which may have resulted in different numbers ofcontacts being reported in CoMix. However, it is unlikely that the large differences observed would be due
only to the reporting methodology of the surveys. Furthermore, we were not able to sample any children, so
child-child contacts had to be imputed from the POLYMOD survey. This weakens the comparability of the twostudies, and future work is planned to directly measurechild-child contacts which will help assess the impact of
this limitation.
We were not able to quantify any additional effectfrom the interventions on transmission, such as reduction in infectiousness by increased handwashing. In
addition, we were not able to calculate the net reproductive number, R, as we did not account for the proportion of the population that is no longer susceptible.
These could all reduce the net reproductive number toJarvis et al. BMC Medicine(2020) 18:124

values lower than estimated in our analysis. This approach further assumes that all age groups contributeequally to transmission, which may not be the case. Assuming flu-like transmission where children are thegroup most responsible for transmission, the contribution of adults to overall R0 would be lower [9]. However,
if children do not play a significant role in transmission,
the significance of adult transmission will be higher.
Therefore, although this survey provides evidence ofoverall contacts in the population reducing which willconsiderably lower R0, lack of knowledge of the relative
contributions of different age groups to overall transmission reduces our ability to precisely determine the exact
reduction in transmission.
Our analysis assumed that direct contacts are an appropriate proxy for effective contacts, and thus, thattransmissibility is equal across age groups (e.g. contact
between a single infected child and susceptible adult is
as likely to result in transmission as contact between asingle infected adult and a susceptible adult). We further
assume that the reduction in non-school contacts inchildren is similar to that observed in adults. Furthermore, we assume that the contact patterns prior to interventions are consistent and of similar magnitude. A
recent study has found significantly lower numbers ofcontacts reported by teenagers compared with the
POLYMOD survey [20]. Decreasing mixing among 5–17 years by 50%, whilst reducing the magnitude of reduction in R0, did not affect the qualitative conclusions
from the analysis.
As of 13 April, the growth rate of reported cases in the
UK appears to be slowing, declining from a 20% increase
per day for the 5 days prior to 24 March to a 7% increase per day for the 5 days prior to 13 April [1]. Thisis consistent with a reduction in the reproduction number; however, it is difficult to correlate the magnitude ofthis reduction with the estimated value of R0. Our approach assumes that individuals within an age group behave the same, and does not account for hospitals andother institutions which will have different disease dynamics. Moreover, due to the UK testing algorithm being focused on people in hospitals and as there seems to
be an increasing problem of nosocomial infection, any
decline in community infection may be counterbalancedby an increase in nosocomial infections. Given that theconfirmed cases are primarily hospital based, this canhave a disproportionate effect on the estimatedreproduction number using crude data (i.e. not split byroute of transmission). Future work is planned to compare the estimates of R0 with the growth rate of the epidemic, accounting for changes in levels of testing,reporting, delays, and transmission context.
This study is planned to continue in the UK for the
next 15 weeks and will be extended to other countries

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including Belgium and the Netherlands. Future analyses
will be able to explore changes in contact patterns during different interventions and may provide early warning signs of changes in contact patterns due tointerventions being lifted or decreasing adherence withrestrictions.

Conclusions
We have shown that behavioural monitoring can give a
rapid insight into transmission of COVID-19 and haveprovided the first evidence that the restrictions adopted
by the UK Government have led to a decrease in transmission of COVID-19.
Table 3 shows compliances with different social distancing measures due to COVID-19. N symptoms shows
the total number of household members who were living
in a household where someone had any of the following
symptoms (fever, aches, shortness of breath, cough), andhow many individuals reported having COVID-19 symptoms themselves. The column Asked to refers to the total
number of people who reported being asked to quarantine or isolate. The column Have been in shows the total
number of people who reported having been in quarantine or isolation for at least 1 day in the 7 days before
the survey.
Supplementary information
Supplementary information accompanies this paper at https://doi.org/10.
1186/s12916-020-01597-8.
Additional file 1. CoMix study questions.
Additional file 2. CoMix study Protocol.
Additional file 3. CoMix additional analyses.
Abbreviations
CI: Confidence interval; IQR: Interquartile range; ONS UK: Office of National
Statistics; SD: Standard deviation
Acknowledgements
CIJ and AG were funded by the Global Challenges Research Fund (ES/P010873/1). KvZ was funded by Elrha’s Research for Health in Humanitarian
Crises [R2HC] Programme, UK Government [Department for International
Development]. KP was funded by the National Institute for Health Research(NIHR; 16/137/109). PK was funded by the Bill & Melinda Gates Foundation(INV-003174). GJR was funded by the National Institute for Health Research
Health Protection Research Unit (NIHR HPRU) in Emergency Preparedness
and Response at King’s College London in partnership with Public Health
England (PHE), in collaboration with the University of East Anglia andNewcastle University. The views expressed are those of the author(s) and notnecessarily those of the NHS, the NIHR, the Department of Health and Social
Care, or Public Health England.
We would like to acknowledge the other members of the London School ofHygiene & Tropical Medicine CMMID COVID-19 modelling group, who contributed to this work. Their funding sources are as follows: Megan Auzenbergs and Kathleen O’Reilly (Bill and Melinda Gates Foundation,
OPP1191821); Graham Medley (NTD Modelling Consortium by the Bill andMelinda Gates Foundation (OPP1184344)); Jon C Emery and Rein M G J Houben (European Research Council Starting Grant, Action Number #757699);
Nicholas Davies (NIHR HPRU-2012-10096); Emily S Nightingale (Bill and Melinda Gates Foundation, OPP1183986; Wellcome Trust, and NIHR); Stefan
Flasche (Sir Henry Dale Fellowship 208812/Z/17/Z); Thibaut Jombart

Jarvis et al. BMC Medicine(2020) 18:124

Page 9 of 10(Research Public Health Rapid Support Team, NIHR Health Protection Research Unit Modelling Methodology); Joel Hellewell, Sam Abbott, James D
Munday, Nikos I Bosse, and Sebastian Funk (Wellcome Trust 210758/Z/18/Z);
Fiona Sun (NIHR; 16/137/109); Akira Endo (The Nakajima Foundation; The
Alan Turing Institute); Alicia Rosello (NIHR: PR-OD-1017-20002); Simon R Procter (Bill and Melinda Gates Foundation, OPP1180644); Fiona Sun (NIHR; 16/
137/109); Adam J Kucharski and Timothy W Russell (Wellcome Trust, 206250/
Z/17/Z); Gwen Knight (UK Medical Research Council, MR/P014658/1); Hamish
Gibbs (Department of Health and Social Care ITCRZ 03010); Quentin Leclerc(Medical Research Council London Intercollegiate Doctoral Training Program
studentship grant no. MR/N013638/1); Billy J. Quilty, Charlie Diamond, Yang
Liu, and Mark Jit (National Institute for Health Research NIHR; 16/137/109);
Yang Liu and Mark Jit (Bill & Melinda Gates Foundation INV-003174); Samuel
Clifford (Sir Henry Dale Fellowship 208812/Z/17/Z); Carl A.B. Pearson (NTD
Modelling Consortium by the Bill and Melinda Gates Foundation
OPP1184344); Rosalind M. Eggo (Health Data Research UK MR/S003975/1);
and Arminder K Deol. We would like to thank Paula Bianca Blomquist forthoughtful critique and discussions during this analysis. This research waspartly funded by the NIHR (16/137/109) using aid from the UK Government
to support global health research. The views expressed in this publication
are those of the author(s) and not necessarily those of the NIHR or the UK
Department of Health and Social Care. We would also like to thank the teamat Ipsos who have been excellent in running the survey, collecting the data,
and allowing for this study to happen at a rapid speed.

2.

Authors’ contributions
WJE, GJR, CIJ, and KvZ conceived of and designed the study. CIJ, KvZ, and
WJE conceived of the analysis. CIJ, KvZ, AG, and KP conducted the analysis.
CIJ and KvZ wrote the manuscript with input and guidance from WJE, AG,
KP, GJR, and PK. The CMMID COVID-19 working group members contributed
to the processing, cleaning, and interpretation of data; interpreted the study
findings; contributed to the manuscript; and approved the work for publication. All authors interpreted the findings, contributed to writing the manuscript, and approved the final version for publication.

10.

Funding
This project was funded by the European Union’s Horizon 2020 Researchand Innovations Programme - project EpiPose (Epidemic Intelligence to
Minimize COVID-19’s Public Health, Societal and Economical Impact, No
101003688).

13.

Availability of data and materials
The code and data used to conduct these analyses are found at https://github.com/jarvisc1/comix_covid-19-first_wave
Ethics approval and consent to participate
Participation in this opt-in study was voluntary, and all analyses were carriedout on anonymised data. The study and method of informed consent wereapproved by the ethics committee of the London School of Hygiene & Tropical Medicine reference number 21795.

3.
4.

5.

6.

7.

8.
9.

11.

12.

14.

15.
16.
17.
18.

Consent for publication
Not applicable
19.
Competing interests
WJE is a member of the Editorial Board of this Journal. Other authors declarethat they have no competing interests.
Author details
1
Centre for Mathematical Modelling of Infectious Diseases, Department of
Infectious Disease Epidemiology, London School of Hygiene & Tropical
Medicine, Keppel Street, London WC1E 7HT, UK. 2Department of
Psychological Medicine, King’s College London, Denmark Hill, London, UK.

20.

21.

22.
Received: 3 April 2020 Accepted: 17 April 2020
23.
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