Jarvis et al. BMC Medicine(2021) 19:52
https://doi.org/10.1186/s12916-021-01924-7

RESEARCH ARTICLE

Open Access

The impact of local and national
restrictions in response to COVID-19 onsocial contacts in England: a longitudinal
natural experiment
Christopher I. Jarvis* , Amy Gimma, Kevin van Zandvoort, Kerry L. M. Wong, CMMID COVID-19 working groupand W. John Edmunds

Abstract
Background: England’s COVID-19 response transitioned from a national lockdown to localised interventions. In
response to rising cases, these were supplemented by national restrictions on contacts (the Rule of Six), then 10 pm
closing for bars and restaurants, and encouragement to work from home. These were quickly followed by a 3-tiersystem applying different restrictions in different localities. As cases continued to rise, a second national lockdown
was declared. We used a national survey to quantify the impact of these restrictions on epidemiologically relevant
contacts.
Methods: We compared paired measures on setting-specific contacts before and after each restriction started andtested for differences using paired permutation tests on the mean change in contacts and the proportion ofindividuals decreasing their contacts.
Results: Following the imposition of each measure, individuals tended to report fewer contacts than they had
before. However, the magnitude of the changes was relatively small and variable. For instance, although earlyclosure of bars and restaurants appeared to have no measurable effect on contacts, the work from home directive
reduced mean daily work contacts by 0.99 (95% confidence interval CI] 0.03–1.94), and the Rule of Six reduced nonwork and school contacts by a mean of 0.25 (0.01–0.5) per day. Whilst Tier 3 appeared to also reduce non-work andschool contacts, the evidence for an effect of the lesser restrictions (Tiers 1 and 2) was much weaker. There may
also have been some evidence of saturation of effects, with those who were in Tier 1 (least restrictive) reducing
their contacts markedly when they entered lockdown, which was not reflected in similar changes in those who
were already under tighter restrictions (Tiers 2 and 3).
Conclusions: The imposition of various local and national measures in England during the summer and autumn of2020 has gradually reduced contacts. However, these changes are smaller than the initial lockdown in March. This
may partly be because many individuals were already starting from a lower number of contacts.
Keywords: COVID-19, Contact survey, Lockdowns, Pandemic, Disease outbreak, Non-pharmaceutical interventions,
United Kingdom, England* Correspondence: Christopher.Jarvis@lshtm.ac.uk
Centre for Mathematical Modelling of Infectious Diseases, Department of
Infectious Disease Epidemiology, London School of Hygiene and Tropical
Medicine, Keppel Street, London WC1E 7HT, UK© The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to thedata made available in this article, unless otherwise stated in a credit line to the data.

Jarvis et al. BMC Medicine(2021) 19:52

BackgroundOn the 23rd of March 2020, England, along with the restof the United Kingdom (UK) went into a national lockdown in response to COVID-19 [1]. This required
people to only leave their house for essential shopping
or medical needs, or to undertake one form of exercise
per day. Educational establishments and non-essential
retail were closed, as were the leisure and hospitality sectors [1]. Many European countries also implemented national lockdowns and the combinations of large-scale
restrictions resulted in marked decreases in contacts,
mobility, and transmission, eventually leading to a reduction in daily cases and deaths [2–5].
As the incidence of cases declined, national restrictions were relaxed [5]. England transitioned to a localised response and only applied more stringent
restrictions to specific areas with rising cases. The firstof these local measures was announced on the 29th ofJune in Leicester [6], then subsequently in other areas,
mostly in the North of England [7]. Local restrictions
vary in magnitude but may include early business closures, take-away services only for bars and restaurants,
bans on meeting with other households, and travel
restrictions.
Alongside local restrictions, in response to rising cases,
several national measures were also introduced. On the
14th of September, the Rule of Six was announced inEngland preventing individuals from meeting in groups
with more than six people [8]. On the 24th of September, it was announced that pubs and restaurants would
be required to close at 10 pm and individuals were encouraged to work from home [9]. Cases continued torise, and the government combined several restrictions
to create a three-tier system ranging from Tier 1(medium risk) to Tier 3 (very high risk). This tier system
was then followed by a second English lockdown from
November 5th to December 2nd [10, 11].
The impact of the less severe measures remains unclear, with cases continuing to rise in most localities
after measures were implemented. Picking up (perhaps
modest) changes in cases, hospitalisations, or deaths
some time after restrictions are introduced would be expected to be difficult. In this paper, we avoid these problems by using repeated measures of individuals’
epidemiologically relevant setting-specific contacts before and after restrictions were imposed to estimate
whether these measures had any effect and if so theirmagnitude.
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

Page 2 of 12

London School of Hygiene & Tropical Medicine Reference number 21795.
Data

We combined data from the English participants of the
UK CoMix survey and information on local and national
restrictions from Gov.uk. Details of the CoMix study including the protocol and survey instrument have been
published previously [2]. In short, CoMix is an online
survey where individuals record details of all their direct(i.e. potentially risky) contacts in the day prior to thesurvey. A direct contact was defined as anyone who wasmet in person and with whom at least one word was exchanged, or anyone with whom the participants had any
sort of skin-to-skin contact. Contacts of individuals
under the age of 18 were collected by asking parents toanswer on behalf of their child. Information is collected
weekly from two alternating, broadly representative,
panels (each about 2500 people in size), with each person surveyed once every 2 weeks.
We extracted the start and end dates of restrictions
and their locations from Gov.uk between August 31stand December 7th, 2020. CoMix participants were considered affected by local restrictions if they reported living within a Lower Tier Local Authority (UKadministrative zone) that was under different restrictions
from those applied nationally. We restricted the data to
16 days before and after each restriction came into place
to allow for two full weeks of survey responses. We thenextracted the closest survey response before and after
each restriction date. Participants with missing survey
responses either side of the start of a restriction were removed, giving two records per person.
Details of restrictions

Local restrictions included a range of rules that were inconsistently applied across regions. Most local restrictions fell into four categories: travel restrictions, nonessential closures, preventing indoor mixing, and discouraging overnight stays. Travel restrictions included
any of essential travel only, travel being discouraged, andresidents banned from leaving their local area. Nonessential closures included places of worship, nonessential retail, gyms, public buildings, personal care services, art venues, and tourist attractions [7].
The Rule of Six prevented individuals from meeting ingroups of six or more indoors and outdoors. The 10 pm
closure stipulated that hospitality venues must be closedwith customers having left the premises by 10 pm. Work
from home relates to individuals being encouraged towork from home where possible.
The three-tier system was created on the 14th of October, each tier built upon the previous tier with Tier 1
being the least stringent and Tier 3 the most [10]. Tier 1

Jarvis et al. BMC Medicine(2021) 19:52(medium risk) roughly equated to the Rule of Six, workfrom home, and 10 pm rule, with the addition of closing
businesses with music and dancing that opens at night.
Tier 2 added no gatherings in indoor space between
households, restricted travel, and increased the numberof venues that closed. Tier 3 prevented meeting in private outdoor spacing with non-household members andrestricted restaurants and bars to table service only, withserving of alcoholic drinks only allowed when consumed
alongside a substantial meal [10].
The second national lockdown was less stringent than
the first as schools remained open, but included closingof pubs, restaurants, gyms, and non-essential shops andasking people to stay at home [11].

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Our study is a longitudinal natural experiment. For eachparticipant, we have one observation prior to the implementation of, and one observation after the restriction.
Observations are at most 16 days from the date of thestart of the restriction. This allows individuals within
our study to be their own control and thus reduces theeffect of between-person variation as well as the effect of
longer-term temporal trends. The types of contact reported were categorised as home-based, work contact,school contact, and in other settings.
We compared the number of contacts before implementation of restrictions to the number of contacts after to assess the impact of (i) local restrictions; (ii) three national
restrictions (1) Rule of Six, (2) 10 pm closure, and (3) workfrom home; (iii) entry into each of Tier 1, Tier 2, and Tier
3; and (iv) entry into the national lockdown from Tier 1,
Tier 2, and Tier 3. To assess the effect of the different restrictions, we concentrated on changes in setting-specificcontacts. For instance, local restrictions and the Tier system are largely targeted at leisure contacts, and the Ruleof Six does not apply for businesses or schools. Hence, forthese two restrictions, we analysed changes in contacts excluding work and school. The 10 pm closure rule requires
restaurants, pubs, and bars to close early, and is therefore
not expected to have a direct effect on contacts made at
home, work, or school. Thus, we excluded contacts in
those settings as the outcome for this restriction and referto remaining contacts as Other contacts. To assess the effect of the work from home advice, we focused on thework contacts of respondents who were employed. During
the second national lockdown, schools remained open,
and therefore, we only excluded contacts made at schoolin assessing its effect.

graphical summaries of participant characteristics forage, gender, employment, and socio-economic status
were created for each restriction, for the change in meancontacts, and for the spatial and temporal variation in
the restrictions. Uncertainty for the mean contacts wascalculated using clustered bootstrapping [15] where
sampling was done per person rather than per observation level to preserve the correlation structure of thedata.
We compared contacts before and during restrictions
by calculating the mean, median, and interquartile range(IQR). The change in contacts was categorised into increased, same (unchanged), and decreased. We calculated the mean of the paired differences in contacts
before and after restrictions and assessed uncertainty by
constructing a 95% confidence interval (95% CI) from10,000 bootstrap samples [15] of the paired differences.
For each restriction, we conducted paired permutation
tests [16] with 50,000 permutations per test. We chosepermutation tests as they are robust to distributional assumptions of the underlying data [14]. In order to preserve the study structure, we calculated the paired
difference by subtracting the reported number of contacts during the restriction from the reported number ofcontacts before the restriction and then randomly changed the sign of each pair. In practice, this means generating a vector of random values taking − 1 and 1 that isof the same length as the number of participants andthen multiplying the change in contacts by this vector.
We decided to calculate two test statistics for each permutation and each restriction: (1) the proportion of individuals whose contacts decreased after restrictions were
implemented, and (2) the mean of the change in contacts
before and after restrictions. The proportion of decreases
is robust to large values and skewed distributions treatinga difference of − 1 and − 1000 in the same way. This measure tests the relative effect of the restriction but does not
estimate the effect size. The mean difference estimates theabsolute effect but is affected by skewed data. In order toreduce the impact of the skewness, we restricted the total
number of contacts to 200 per person per day for thecomparison of the means only.
We conducted further assessment of the restrictions
by age group for the Rule of Six and the 10 pm rule asthese restrictions are likely to have greater potential ofan effect in younger individuals who are more likely to
be mobile, asymptomatic if infected, and not be shielding. These analyses were stratified by age groups 5–17,
18–39, 40–59, and 60 +.

Statistical analysis

Results

R version 4.0.0 was used for all analyses and the codeand data are available on github (see the “Availability ofdata and materials” section) [12–14]. Descriptive andParticipant characteristics

Study design

There were 3884 participants included in the analysis for
the Rule of Six: 3887 for 10 pm closure, 1408 for workJarvis et al. BMC Medicine(2021) 19:52

from home, and 572 participants for local restrictions(Table 1). There were 2415 entering into Tier 1, 1654
entering Tier 2, and 368 entering Tier 3. Furthermore,
there were 2095 leaving Tier 1 into national lockdown,
1445 leaving Tier 2 into national lockdown, and 323
leaving Tier 3 into national lockdown. The age distributions of the samples for Rule of Six, 10 pm closure, local
restrictions, entry to tiers, and national lockdown weresimilar with over 30% of the samples being between 50
and 69 years of age in all nine analyses. The work fromhome category by definition only included participants
18 years of age or older and nearly 70% of participants
were between 30 and 59. The gender split was close to50% for all restrictions. Excluding the work from homeanalysis, around 40% of participants were employed foreach restriction (which reflects the broad age range ofthe sample, including children and the elderly). Socioeconomic status was consistent across each analysis
samples with lowest numbers in the A - upper middle
class, and E - lower level of subsistence categories andthe modal group being C1 - lower middle class for all restrictions (Table 1).
All contacts in adults and restrictions

Restrictions were applied nationally in England fromMarch through to June (Fig. 1). In the summer, restrictions were reduced, and local restrictions were applied
relating to travel, non-essential closures, no indoor mixing, and discouraging overnight stays (Fig. 1c). These restrictions were mostly applied in the North of England(Fig. 1a). The relaxing of restrictions in August coincides
with an increase in the mean contacts for adults, withcontacts gradually reducing again from September toNovember as restrictions became more stringent andwidespread (Fig. 1b). Following the second national lockdown, mean daily reported contacts returned to similarlevels as in July. Figure 1 presents mean contacts overtime for adults only, as data on children were not collected throughout this period of the survey, and as the
2nd national lockdown did not involve school closure.
Distribution of setting-specific contacts

The setting-specific contacts were positively skewed for
all restrictions (Fig. 2a, Table 2). The Rule of Six andlocal restrictions had similar distributions with themodal response being one contact before and the restrictions, whereas work from home and the 10 pm rule
had a majority of individuals reporting zero contacts.
The distributions for entry to tiers and exit from tiers to
lockdown were similar with a median of 2 and IQR of 1
to 3, excluding exit tier 1 to lockdown, which had an
IQR of 1 to 4. Overall, the magnitudes of the change incontacts were small, and for most individuals, the number of reported contacts did not change after each

Page 4 of 12

measure was introduced (Table 2). In order to show thepatterns in the data, we restricted the axes and removed
zero values in Figs. 2b, 3a, b. These graphs are reproduced in the Additional file 1: Figure S1A and S1B and
Additional file 2: Figure S2A and S2B without removalof zero values or restriction of axes for comparison.
National restrictions
Rule of Six

We compared non-work and non-school contacts for
3884 individuals before and after the Rule of Six cameinto effect. There was very strong evidence (p < 0.001) tosuggest that more people reduced their contacts (excluding work and school) following the Rule of Six restriction than expected due to chance, with 1314 (33.8%)
recording fewer contacts compared to 997 (25.7%) recording a greater number of contacts. However, in mostparticipants, 1573 (40.5%) recorded the same number ofcontacts and the median number of contacts was 2 (IQR
1 to 3) before and after the introduction of the measure.
There was a slight suggestion (p = 0.05) of a small reduction (− 0.25; − 0.5 to − 0.01) of mean non-work and nonschool contacts per day (Table 2). Age-stratified analysis(Table 3) suggests that the Rule of Six had the greatest
effect on the contact patterns of young adults (18–39
years), who reduced non-work and non-educational contacts by a mean of − 0.59 (− 1.09 to − 0.04).
Ten pm closure

We compared ‘other’ contacts (excluding home, work,or school) among 3887 participants before and after the
10 pm closure. There was very strong evidence (p <0.001) to suggest that more people reduced their contacts following the 10 pm rule than expected due tochance, with 990 (25.5%) recording fewer contacts compared to 843 (21.7%) recording a greater number of contacts. However, more than half of the participants 2054
(52.8%) recorded the same number of contacts and the
median number of contacts was very low (0; IQR 0 to 1)
before and after the 10 pm rule. The data were consistent with no absolute effect (p = 0.915) with the change
in mean ‘other’ contacts estimated as 0.01 (− 0.23 to0.23) (Table 2). Subgroup analysis suggested inconsistentpatterns by age group (Table 3), which might be expected if overall there is no evidence of changes in contacts following this measure.
Work from home

Over two thirds of participants 933 (66.3%) had thesame number of work contacts before and after being
encouraged to work from home. Despite this, the datastrongly suggest (p = 0.001) that a greater number reduced their work contacts after the restriction came into
place than would be expected due to chance. Differences

(2021) 19:52

Jarvis et al. BMC Medicine

Page 5 of 12

Table 1 Participants characteristics in the CoMix survey for each restriction
Restrictions

Entry into
Rule of
Six

Work from
home

Local
lockdown

Tier 1N (col %) N (col %)N (col %)N (col %)

3884

3887

1408

0–4

129
(3.3%)

142 (3.7%)

5–11

214
(5.5%)

12–17

Exit from tier to lockdown
Tier 2Tier 3N (col %) N (col %) N (col%)

N (col %) N (col%)

N (col
%)

572

2415

1654

368

2095

1455

323

0

22 (3.9%)

96 (4.0%)65 (4.0%)

12(3.3%)

53 (2.5%)

49(3.4%)

13(4.0%)

275 (7.1%)

054 (9.5%)151(6.3%)

117(7.1%)

36(9.9%)

120(5.8%)

85(5.9%)

31(9.7%)

261(6.8%)

291 (7.5%)

059 (10.4%)

202(8.4%)

121(7.4%)

48(13.2%)

149(7.1%)

101(7.0%)

41(12.8%)

18–29

364(9.4%)

384 (9.9%)222 (15.8%)

61 (10.7%)

197(8.2%)

161(9.8%)

23(6.3%)

158(7.6%)

124(8.6%)

20(6.2%)

30–39

462(12.0%)

432 (11.2%) 308 (21.9%)

65 (11.4%)240(10.0%)

191(11.6%)

42(11.6%)

194(9.3%)

153(10.6%)

34(10.6%)

40–49

495(12.8%)

531 (13.7%) 363 (25.8%)

89 (15.6%)

326(13.6%)

219(13.3%)

47(12.9%)

288(13.8%)

200(13.8%)

42(13.1%)

50–59

708(18.3%)

613 (15.8%) 322 (22.9%)

90 (15.8%)

402(16.7%)

277(16.9%)

63(17.4%)

355(17.0%)

262(18.1%)

49(15.3%)

60–69

723(18.7%)

751 (19.4%) 174 (12.4%)

88 (15.5%)

449(18.7%)

309(18.8%)

50(13.8%)

428(20.5%)

291(20.1%)

48(15.0%)

70+

506(13.1%)

449 (11.6%) 19 (1.3%)

41 (7.2%)

341(14.2%)

181(11.0%)

42(11.6%)

341(16.3%)

182(12.6%)

43(13.4%)

Missing

22

19

–

3

11

13

5

9

8

2

Female2013(52.0%)

2004(51.6%)

718 (51.1%)

277 (48.7%)1252(52.0%)

890(54.0%)

179(48.8%)

1072(51.3%)

758(52.2%)

156(48.4%)

Male

1861(48.0%)

1877(48.4%)

688 (48.9%)292 (51.3%)

1156(48.0%)

759(46.0%)

188(51.2%)

1018(48.7%)

694(47.8%)

166(51.6%)

Missing

10

6

2

3

7

5

1

5

3

1

Yes

1487
(38.3%)

1433(36.9%)

1408 (100.0%)

220 (38.5%)

882(36.5%)

608(36.8%)

134(36.4%)

761(36.3%)

521(35.8%)

112(34.7%)

No

2397(61.7%)

2454(63.1%)

0352 (61.5%)

1533(63.5%)

1046(63.2%)

234(63.6%)

1334(63.7%)

934(64.2%)

211(65.3%)A - Upper middle
class

200
(5.1%)

214 (5.5%)

72 (5.1%)

24 (4.2%)

143(5.9%)

89 (5.4%)

14(3.8%)

119(5.7%)

79(5.4%)

9 (2.8%)B - Middle class1061(27.3%)

1033(26.6%)

394 (28.0%)

161 (28.1%)

622(25.8%)

418(25.3%)

90(24.5%)

554(26.4%)

375(25.8%)

85(26.3%)

C1 - Lower middle
class

1285
(33.1%)

1332(34.3%)

536 (38.1%)

184 (32.2%)

812(33.6%)

596(36.0%)

130(35.3%)

731(34.9%)

529(36.4%)

115(35.6%)

C2 - Skilled working
class

534
(13.7%)

529 (13.6%) 197 (14.0%)

85 (14.9%)

343(14.2%)

227(13.7%)

50(13.6%)

278(13.3%)

206(14.2%)

40(12.4%)

D - Working class571(14.7%)

556 (14.3%) 203 (14.4%)

82 (14.3%)

377(15.6%)

221(13.4%)

55(14.9%)

304(14.5%)

176(12.1%)

47(14.6%)

E - Lower level ofsubsistence

233(6.0%)

223 (5.7%)

36 (6.3%)

118(4.9%)

103(6.2%)

29(7.9%)

109(5.2%)

90(6.2%)

27(8.4%)

Total

10 pm
closureTier 2Tier 3Tier 1

Age groups

Gender

Employed

Socio-economic status

6 (0.4%)

Jarvis et al. BMC Medicine(2021) 19:52

Page 6 of 12

Fig. 1 a Number of restrictions by English Lower Tier Local Authority (LTLA) over time. b Mean contacts for adults from CoMix over time. c
Heatmap of number of LTLA areas affected by each restriction over time. Graph A shows the geographical distribution of the restriction by LTLA
region in England overtime. The number of restrictions represents the maximum number of restrictions applied at any time during a single
month. Graph B shows the mean number of contacts in all settings with a cap of 200 contacts per person, 2000 bootstrap samples were used togenerate 95% uncertainty intervals with sampling done at the participant level to reflect clustering of repeated measures. Graph C shows atemporal heatmap of the number of LTLA areas that were affected by each restriction. *Rule of Six and 10 pm rules are included in the firstnational lockdown as more stringent rules were applied with all restaurants and bars being closed and no meetings allowed with anyother households

in work contacts were highly skewed with forty participants reporting a difference of more than 50 contacts,
yet the 25th and 50th quantile of the difference being
zero (Fig. 2, Table 2). The data were compatible (p =0.05) with a reduction in the mean work contacts.
Though there was large uncertainty around the point estimate, it was far from zero (− 0.99 contacts per day,
95% CI − 1.94 to − 0.03; Table 2).

Local restrictions

There was strong evidence (p < 0.001) that following
local restrictions more participants reduced their nonwork and non-school contacts than would be expected
due to chance. Of the 572 participants, 197 (34.4%) individuals reported fewer contacts, 123 (21.5%) reported
greater contacts, and 252 (44.1%) reported the same
number of contacts. On average, participants reported
0.69 (0.17 to 1.25; p = 0.004) fewer non-work and non-school contacts compared to before the restrictions, corresponding to a relative reduction of 21% (5% to 40%).

Entry into tiers

We compared non-work and non-school contacts for2415 individuals before and after entry into Tier 1, 1654
for Tier 2, and 368 for Tier 3 (Table 2). There was astrong suggestion that more people reduced their contacts than would be expected due to chance after entry
into each tier, though there was a higher percentage of
people who increased their contacts when entering into
Tier 1 compared to entry in other levels. Indeed, thedata were consistent with no reduction in mean contacts
after entering into Tier 1 or Tier 2 with change in reported contacts close to zero. There was a suggestion ofa reduction in daily contacts from 3.09 to 2.32 following
entry into Tier 3 (p = 0.067), but there are only 368 observations in this category. The median number of dailyJarvis et al. BMC Medicine(2021) 19:52

Page 7 of 12

Fig. 2 a The distribution of the number of setting-specific contacts before and after each restriction came into place. b Change in contacts foreach restriction. c The proportion of changes comparing before and after the restrictions started. Graph A shows the distribution of settingspecific contacts before and after each restriction came into place. Colours represent whether the change in contacts increased (brown),
decreased (yellow), or stayed the same (orange). Values greater than 10 are not shown on the graph. Graph B shows the distributions of thedifference in contacts, with zero values removed to ease comparison between increases and decreases. Differences of magnitude greater than 10
are not shown. Graph C shows the proportions of changes in contacts due to each restriction. For most participants, the number of contacts
remained unchanged (yellow) after restrictions. Additional file 1 Figure S1A and S1B show graphs 2A and 2B without the restrictions on the axes
and with zero values included

contacts remained fixed at 2 (IQR 1 to 3) before andafter entry into all tiers.
Exit from tiers into national lockdown

We compared non-school contacts for 2095 individuals before and during the national lockdown enteringfrom Tier 1, 1455 entering from Tier 2, and 323 entering from Tier 3. The data were consistent withmore individuals reducing their contacts than expected due to chance. The largest difference was observed for those entering lockdown form Tier 1 with
750 (35.8%) reducing their contacts versus 390

Jarvis et al. BMC Medicine(2021) 19:52

Page 8 of 12

Table 2 Summary of permutation test on the proportion of individuals with decreased contacts and paired mean difference before
and after restrictions
Comparison with proportion decreased
Restriction

Contacts

N

AdultsChildren

Decreased

Same

Increased

P valueLocal

exclude work and school

572

434

138

197 (34.4%)

252 (44.1%)

123 (21.5%)< 0.001

ROS

exclude work and school

3884

3258

626

1314 (33.8%)

1573 (40.5%)997 (25.7%)< 0.001

10 pm

other

3887

3160

727

990 (25.5%)2054 (52.8%)

843 (21.7%)< 0.001

WFH

work

1408

1408

0

288 (20.5%)933 (66.3%)

187 (13.3%)< 0.001

T1 entry

exclude work and school

2415

1955

460

752 (31.1%)

993 (41.1%)

670 (27.7%)

0.017

T2 entry

exclude work and school

1654

1338

316

468 (28.3%)

823 (49.8%)363 (21.9%)< 0.001

T3 entry

exclude work and school

368

267

101

103 (28.0%)

188 (51.1%)

77 (20.9%)

0.034

T1 exit to LD

exclude school

2095

1764

331

750 (35.8%)955 (45.6%)

390 (18.6%)< 0.001

T2 exit to LD

exclude school

1455

1212

243

428 (29.4%)

732 (50.3%)295 (20.3%)< 0.001

T3 exit to LD

exclude school

323

236

87

85 (26.3%)

173 (53.6%)

65 (20.1%)

0.062

Comparison in mean difference

Median (IQR)

Mean

Restriction

Contacts

BeforeAfter

BeforeAfter

Difference (95% CI)

P value

Local

Exclude work and school

2 (1 to 4)

2 (1 to 3)

3.18

2.49

− 0.69 (− 1.25 to − 0.17)

0.004

ROS

Exclude work and school

2 (1 to 3)

2 (1 to 3)

2.9

2.66

− 0.25 (− 0.5 to − 0.01)

0.045

10 pm

Other

0 (0 to 1)

0 (0 to 1)

1.37

1.38

0.01 (− 0.23 to 0.23)

0.915

WFH

Work

0 (0 to 1)

0 (0 to 0)

4.62

3.62

− 0.99 (− 1.94 to − 0.03)

0.042

T1 entry

Exclude work and school

2 (1 to 3)

2 (1 to 3)

2.79

2.66

− 0.13 (− 0.39 to 0.11)

0.305

T2 entry

Exclude work and school

2 (1 to 3)

2 (1 to 3)

2.42

2.56

0.14 (− 0.17 to 0.55)

0.473

T3 entry

Exclude work and school

2 (1 to 3)

2 (1 to 3)

3.09

2.32

− 0.77 (− 1.97 to − 0.03)

0.067

T1 exit to LD

Exclude school

2 (1 to 4)

2 (1 to 3)

4.21

2.81

− 1.40 (− 2.03 to − 0.85)< 0.001

T2 exit to LD

Exclude school

2 (1 to 3)

1 (1 to 3)

3.4

2.97

− 0.42 (− 1.13 to 0.33)

0.247

T3 exit to LD

Exclude school

2 (1 to 3)

2 (1 to 3)

3.08

3.54

0.46 (− 0.28 to 1.41)

0.343

Two-sided p value calculated by counting the number of permutations where the magnitude of the test statistics is greater than the observed test statistics, anddividing by the number of permutations
ROS Rule of Six, WFH encouraged to work from home, LD lockdown, T1 Tier 1, T2 Tier 2, T3 Tier 3, IQR inter-quartile range(18.6%) increasing them (Table 2). This was consistent with the strong evidence (p < 0.001) for a reduction of 1.40 (0.85 to 2.03) in the mean daily numberof contacts when entering from Tier 1 to lockdown.
The effect of moving from Tier 2 or Tier 3 to lockdown was less pronounced, though there were few
observations for the estimate of Tier 3 to lockdown.

Discussion
Along with many other countries, England transitioned
from a national lockdown approach to more localised interventions with less restrictive national measures andsubsequently reverted to a national lockdown in the autumn of 2020. We determine that the impact of thesemeasures is mixed: the imposition of local measures(which were very varied in different places) and the Ruleof Six probably led to modest reductions in contacts; instructions to work from home if possible led to a larger
reduction in contact, whereas there is little to no evidence that the 10 pm closing time for bars and restaurants had any appreciable effect. The adoption of the tiersystem was similarly mixed, with Tier 1 and 2 having little impact on mean contacts, but Tier 3 (the most stringent) reducing the mean daily reported number ofcontacts. The subsequent imposition of the lockdown
appears to have reduced contacts in those individuals
who were previously under Tier 1 (the lightest restrictions), but the data is less supportive of an effect in individuals who were already under tighter restrictions(Tiers 2 and 3), potentially because as it was more difficult for these participants to further reduce theircontacts.
In absolute terms, these changes in mean contacts are
relatively small. However, the relatively small size of theabsolute effects in this study may be more indicative that
restrictions were applied at a point when individuals
already had lowered their contacts, as opposed to an indication that the restrictions would not have an effect.
For instance, the appeal to work from home only reduced the mean daily work contacts by about 1, thougha larger sized effect would perhaps have been difficult toachieve as working from home was already relatively

Jarvis et al. BMC Medicine(2021) 19:52

Page 9 of 12

Fig. 3 a Change in contacts after entry into each tier. b Change in contacts after entry into national lockdown from each tier. c Proportion ofchanges in contacts after entry into each tier. d Proportion of changes in contacts after entry into lockdown from each tier. Graph A and B show
the distributions of the difference in contacts. Zero values are not shown to ease comparison between increases and decreases. Differences ofmagnitude greater than 10 are not shown. Graph C and D show the proportion of change in contacts due to each restriction. For the majority ofpeople, the number of contacts remained unchanged after entry into each tier or exit from each tier to lockdown. Additional file 2 Figure S2A
and S2B show graphs 3B and 3B without the restrictions on the axes and with zero values included

commonplace at the time the measure was implemented[17]. To put these changes in context, the full national
lockdown in March reduced the average daily contacts
from an estimated 10.8 to 2.8 [2]. This 74% reduction, inturn, reduced the effective reproduction number (R0) of
COVID-19 from about 2.6 before lockdown to about 0.6
after lockdown [2]. The impact on R0 from the relatively

small reduction in mean work contacts made under thevarious restrictions discussed here would likely have amuch more marginal impact on R0.
Determining the epidemiological effect of restrictions
has proved challenging. This is because of delays between the imposition of measures and their effect on reported cases, hospitalisations, and deaths. Furthermore,Jarvis et al. BMC Medicine(2021) 19:52

Page 10 of 12

Table 3 Summary of permutation test on the proportion of individuals with decreased contacts and paired mean difference before
and after 10 pm and Rule of Six restrictions by age
Comparison with proportion decreased
Restriction

Contacts

N

AdultsChildren

Decreased

Same

Increased

P valueROS
5–17

Exclude work and school

464

0

464

167 (36%)179 (38.6%)

118 (25.4%)

0.0022

18–39

Exclude work and school

816

816

0

291 (35.7%)

343 (42%)182 (22.3%)< 0.001

40–59

Exclude work and school

1193

1193

0

396 (33.2%)

488 (40.9%)309 (25.9%)0.000560+

Exclude work and school

1225

1225

0

403 (32.9%)475 (38.8%)347 (28.3%)

0.0219

5–17

Exclude work and school

550

0

550

167 (30.4%)

239 (43.5%)

144 (26.2%)

0.1062

18–39

Exclude work and school

813

813

0

243 (29.9%)376 (46.2%)

194 (23.9%)0.0103

40–59

Exclude work and school

1134

1134

0

350 (30.9%)507 (44.7%)

277 (24.4%)

0.002

60+

Exclude work and school

1196

1196

0

395 (33%)453 (37.9%)

348 (29.1%)

0.0452After

Difference (95% CI)

P value

10 pm

Comparison in mean difference

Median (IQR)

Mean

Restriction

BeforeBeforeContacts

After

ROS
5–17

Exclude work and school

3 (2 to 4)

3 (2 to 4)

3.93

4.09

0.17 (− 0.35 to 0.76)

0.5668

18–39

Exclude work and school

2 (1 to 3)

2 (1 to 3)

2.89

2.3

− 0.59 (− 1.09 to − 0.04)

0.0183

40–59

Exclude work and school

2 (1 to 3)

2 (1 to 3)

2.61

2.31

− 0.3 (− 0.67 to 0.03)

0.105560+

Exclude work and school

2 (1 to 3)

2 (1 to 3)

2.56

2.6

0.04 (− 0.39 to 0.52)

0.8797

5–17

Exclude work and school

3 (2 to 4)

3 (2 to 4)

3.85

4.84

0.98 (0.28 to 1.81)

0.0115

18–39

Exclude work and school

2 (1 to 3)

2 (1 to 3)

2.7

2.57

− 0.13 (− 0.72 to 0.5)

0.649

40–59

Exclude work and school

2 (1 to 3)

1 (1 to 3)

2.4

2.54

0.14 (− 0.37 to 0.65)

0.618360+

Exclude work and school

2 (1 to 3)

1 (1 to 3)

2.54

2.09

− 0.45 (− 0.88 to − 0.12)

0.003910 pm

Two-sided p value calculated by counting the number of permutations where the magnitude of the test statistics is greater than the observed test statistics, anddividing by the number of permutations
ROS Rule of Six, IQR inter-quartile range

reported numbers of cases might be biased upwards inareas of local restrictions if additional efforts are put inplace to find and test cases in these regions. Estimating
the counterfactual—how many cases might have occurred without the restrictions—is also very difficult to
do. For these reasons, evidence on the effect of local andnational restrictions is weak. This study takes a different
approach. Contacts might be expected to change immediately after restrictions are in place and would be lessaffected by changes in case finding. Furthermore, thelongitudinal panel nature of the data allows for individuals to act as their own temporal control group, making
it easier to pick up relatively small changes in contact
patterns.
This work has several limitations. We had to groupseveral types of measures used in local restrictions andtherefore the effect that we see is a combination of arange of interventions. Individuals may also not accurately report their contacts, due to recall or social desirability bias. A further limitation is that restrictions were
not randomly allocated and thus the effects seen may be

due to other confounding factors. However, we did use a
repeated measure on the same individuals, which will reduce between-person variability, though confounding
factors could remain constant on individuals and affect
the generalisability of results. The contact data isbounded at zero and skewed; therefore, using the meancan be a less relevant summary measure; this is why wealso performed a permutation test that focused on thesign of the difference rather than the magnitude. Furthermore, we did not distinguish between the length oftime spent with different contacts. Finally, as contacts
decline, the possibility for individuals to further reduce
their social interactions decreases. Any changes in contacts would therefore be expected to be small and would
require a very large survey to quantify accurately.
Our study design—concentrating on contacts 16 days
before and after new measures—attempted to reduce theeffect of temporal trends in contacts. However, it is likelythat it did not do this entirely. In addition, the relatively
rapid change in policies over the autumn means that someof the effect attributed to one of the interventions mayJarvis et al. BMC Medicine(2021) 19:52

have actually been caused by one of the others. We looked
at setting specific contacts (e.g. work and school) to try tolimit these potential spill-overs but cannot be sure that we
eliminated them entirely. A shorter period of study (e.g. 1
week before or after the new measures) would reduce
both of these potential issues, but would also significantly
reduce our sample size (as data is collected in alternating
weeks), and therefore power to detect a difference.
Despite these limitations, we have attempted to provide insight into the highly relevant issue of whether different restrictions in response to COVID-19 work and if
so, how effective they are. We only focused on one
metric of epidemiological relevant setting-specific contacts, though the impact of the different restrictions will
have broader societal implications that need to be considered for policy change.
Future work could assess whether restrictions reduce theamount of time spent with individuals, as may well be thecase for the 10 pm rule. Further exploration of the effect ofrestrictions on different age groups and the potential of regional adherence to the national restrictions could help disentangle whether lack of effects was due to sampling biases
rather than lack of effectiveness of restrictions.

Conclusions
We have demonstrated that behavioural monitoring canallow the rapid evaluation of the impact of national and local
restrictions on COVID-19 transmission. Although many ofthese restrictions appear to have led to behavioural change,
the magnitude of these changes appears to be small.
Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s12916-021-01924-7.
Additional file 1: Figure S1A. A: The distribution of the number ofsetting-specific contacts before and after each restriction came into place.
Figure S1B. Change in contacts for each restriction.
Additional file 2: Figure S2A. Change in contacts after entry into eachtier. Figure S2B. Change in contacts after entry into national lockdown
from each tier.
Abbreviations
CI: Confidence interval; IQR: Interquartile range; UK: United Kingdom
Acknowledgements
The following authors were part of the Centre for Mathematical Modelling of
Infectious Disease 2019-nCoV working group. Each contributed in processing,cleaning and interpretation of data, interpreted findings, contributed to themanuscript, and approved the work for publication: Kaja Abbas, C Julian
Villabona-Arenas, Kathleen O’Reilly, Matthew Quaife, Alicia Rosello, Adam J
Kucharski, Hamish P Gibbs, Katherine E. Atkins, Rosanna C Barnard, Nikos I
Bosse, Simon R Procter, Sophie R Meakin, Fiona Yueqian Sun, Sam Abbott,James D Munday, Timothy W Russell, Stefan Flasche, Katharine Sherratt, Rosalind M Eggo, Nicholas G. Davies, Billy J Quilty, Megan Auzenbergs, Joel Hellewell, Thibaut Jombart, Yalda Jafari, Quentin J Leclerc, Rachel Lowe, Anna M
Foss, Mark Jit, Arminder K Deol, Stéphane Hué, Gwenan M Knight, Akira
Endo, Kiesha Prem, Jon C Emery, Samuel Clifford, Graham Medley, Sebastian
Funk, Frank G Sandmann, Damien C Tully, Carl A B Pearson, Georgia R Gore-

Page 11 of 12

Langton, Alicia Showering, Rein M G J Houben, Emily S Nightingale, Petra
Klepac, Naomi R Waterlow, Yung-Wai Desmond Chan, James W Rudge, David
Simons, Charlie Diamond, Jack Williams, Oliver Brady, Yang Liu.
The following funding sources are acknowledged as providing funding for
the working group authors. Alan Turing Institute (AE). BBSRC LIDP (BB/M009513/1: DS). This research was partly funded by the Bill & Melinda Gates
Foundation (INV-001754: MQ; INV-003174: KP, MJ, YL; NTD Modelling Consortium OPP1184344: CABP, GFM; OPP1180644: SRP; OPP1183986: ESN;OPP1191821: KO’R, MA). BMGF (OPP1157270: KA). DFID/Wellcome Trust (Epidemic Preparedness Coronavirus research programme 221303/Z/20/Z: CABP).
DTRA (HDTRA1-18-1-0051: JWR). ERC Starting Grant (#757699: JCE, MQ,
RMGJH). This project has received funding from the European Union’s Horizon 2020 research and innovation programme - project EpiPose (101003688:
KP, MJ, PK, RCB, YL). This research was partly funded by the Global Challenges Research Fund (GCRF) project ‘RECAP’ managed through RCUK and
ESRC (ES/P010873/1: TJ). HDR UK (MR/S003975/1: RME). MRC (MR/N013638/1:
NRW). Nakajima Foundation (AE). This research was partly funded by the National Institute for Health Research (NIHR) using UK aid from the UK Government to support global health research. The views expressed in thispublication are those of the author(s) and not necessarily those of the NIHR
or the UK Department of Health and Social Care (16/136/46: BJQ; 16/137/
109: BJQ, CD, FYS, MJ, YL; Health Protection Research Unit for Immunisation
NIHR200929: NGD; Health Protection Research Unit for Modelling Methodology HPRU-2012-10096: TJ; NIHR200908: RME; NIHR200929: FGS, MJ; PR-OD1017-20002: AR). Royal Society (Dorothy Hodgkin Fellowship: RL;
RP\EA\180004: PK). UK DHSC/UK Aid/NIHR (ITCRZ 03010: HPG). UK MRC (LID
DTP MR/N013638/1: GRGL, QJL; MC_PC_19065: NGD, RME, SC, TJ, YL; MR/
P014658/1: GMK). Authors of this research receive funding from UK Public
Health Rapid Support Team funded by the United Kingdom Department of
Health and Social Care (TJ). Wellcome Trust (206250/Z/17/Z: AJK, TWR;
206471/Z/17/Z: OJB; 208812/Z/17/Z: SC, SF; 210758/Z/18/Z: JDM, JH, KS, NIB,
SA, SFunk, SRM). No funding (AKD, AMF, AS, CJVA, DCT, JW, KEA, SH, YJ,
YWDC).
We would also like to thank the team at Ipsos who have been excellent inrunning the survey, collecting the data, and allowing for this study tohappen at a rapid speed. We would like to thank Tim P Morris for feedbackon the manuscript. We thank TW and FC, for providing data and insight on
local restrictions.

Authors’ contributions
KvZ, AG, WJE, and CIJ designed the study protocol and the questionnaire. CIJ
and WJE conceived of the analysis. CIJ conducted the analysis and wrote thefirst draft of the manuscript with feedback from all other authors. AG, KW,
KvZ, and CIJ were involved in collecting and managing data. All authors readand approved the final manuscript.

Funding
CoMix is funded by the EU Horizon 2020 Research and Innovations
Programme - project EpiPose (Epidemic Intelligence to Minimize COVID-19’s
Public Health, Societal and Economical Impact, No 101003688) and by the
Medical Research Council (Understanding the dynamics and drivers of the
COVID-2019 epidemic using real-time outbreak analytics MC_PC 19065). CIJ
and WJE receive funding from the Global Challenges Research Fund (GCRF)
project ‘RECAP’ managed through RCUK and ESRC (ES/P010873/1). KvZ is
supported by Elrha’s Research for Health in Humanitarian Crises (R2HC)
Programme, which aims to improve health outcomes by strengthening theevidence base for public health interventions in humanitarian crises. The
R2HC programme is funded by the UK Government (Foreign, Commonwealth and Development Office, previously DFID), the Wellcome Trust, andthe UK National Institute for Health Research (NIHR). The following funding
sources are acknowledged as providing funding for the named authors: Elrha
R2HC/UK FCO/Wellcome Trust; and the National Institute for Health Research(NIHR) using UK aid from the UK Government to support global health research, NIHR (PR-OD-1017-20002: WJE). UK MRC (MC_PC_19065: AG, WJE).

Availability of data and materials
The code and data used to conduct these analyses are found at https://github.com/jarvisc1/comix_uk_covid_restrictions

Jarvis et al. BMC Medicine(2021) 19:52

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 wasapproved by the ethics committee of the London School of Hygiene & Tropical Medicine Reference number 21795. The research was conducted in accordance with the Declaration of Helsinki.
Consent for publication
Not applicableCompeting interests
The authors declare no competing interests.
Received: 15 October 2020 Accepted: 21 January 2021

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