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RESEARCH ARTICLE
Public acceptance of privacy-encroaching
policies to address the COVID-19 pandemic in
the United Kingdom
Stephan LewandowskyID
1,2
*, Simon Dennis
3
, Andrew Perfors
3
, Yoshihisa Kashima
3
,
Joshua P. WhiteID
3
, Paul Garrett
3
, Daniel R. Little
3
, Muhsin Yesilada
1
1University of Bristol, Bristol, United Kingdom, 2University of Western Australia, Perth, Australia, 3University
of Melbourne, Melbourne, Australia
*stephan.lewandowsky@bristol.ac.uk
Abstract
The nature of the COVID-19 pandemic may require governments to use privacy-encroach-
ing technologies to help contain its spread. One technology involves co-location tracking
through mobile Wi-Fi, GPS, and Bluetooth to permit health agencies to monitor people’s
contact with each other, thereby triggering targeted social-distancing when a person turns
out to be infected. The effectiveness of tracking relies on the willingness of the population to
support such privacy encroaching measures. We report the results of two large surveys in
the United Kingdom, conducted during the peak of the pandemic, that probe people’s atti-
tudes towards various tracking technologies. The results show that by and large there is
widespread acceptance for co-location tracking. Acceptance increases when the measures
are explicitly time-limited and come with opt-out clauses or other assurances of privacy.
Another possible future technology to control the pandemic involves “immunity passports”,
which could be issued to people who carry antibodies for the COVID-19 virus, potentially
implying that they are immune and therefore unable to spread the virus to other people.
Immunity passports have been considered as a potential future step to manage the pan-
demic. We probe people’s attitudes towards immunity passports and find considerable sup-
port overall, although around 20% of the public strongly oppose passports.
Introduction
The COVID-19 pandemic has changed nearly all aspects of people’s lives around the world. In
the absence of a vaccine or successful treatments, the only tools available to control the pan-
demic are behavioral in nature [1]. Countries that have successfully “flattened the curve” have
primarily resorted to social-distancing measures, such as encouraging or forcing people to
stay home, restricting public or even private gatherings, restricting movement through public
spaces, cancelling large public events, and so on.
However, social distancing cannot be sustained indefinitely, which raises the question
about how social life can resume without reigniting the pandemic. This question has become
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OPEN ACCESS
Citation: Lewandowsky S, Dennis S, Perfors A,
Kashima Y, White JP, Garrett P, et al. (2021) Public
acceptance of privacy-encroaching policies to
address the COVID-19 pandemic in the United
Kingdom. PLoS ONE 16(1): e0245740. https://doi.
org/10.1371/journal.pone.0245740
Editor: Andrew Soundy, University of Birmingham,
UNITED KINGDOM
Received: August 25, 2020
Accepted: January 6, 2021
Published: January 22, 2021
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
process; therefore, we enable the publication of
all of the content of peer review and author
responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0245740
Copyright: ©2021 Lewandowsky et al. This is an
open access article distributed under the terms of
the Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The data are available
at https://osf.io/42wj6/ (first wave) and https://osf.
io/pw5yj/(second wave).
particularly acute at the time of this writing (November 2020), as countries around the world
are struggling to manage a second wave of the pandemic after seemingly bringing the virus
under control during the boreal summer. At least two potential technological or biomedical
options have been developed or proposed to assist with management of the relaxation of social
distancing. The first option involves the use of tracking technologies, which monitor people’s
interactions and send alerts to people who have been in proximity to others who turn out to be
infected [2]. This technology has matured to the point where several countries, among them
Singapore, Germany, and Australia, rolled out tracking technologies at scale during the boreal
summer, with other countries including the U.K. following suit more recently. The second
option remains impractical at present and involves issuing people with “immunity passports”
if they test positive for antibodies, indicating their presumed immunity to the virus. Immunity
passports would bestow privileges on their bearer, such as exemption from social distancing
measures.
This article reports the results from two large-scale surveys conducted in the United King-
dom during the height of the first wave of the pandemic (March-April 2020) that probed the
public’s attitude towards both of these privacy-encroaching options to combat the pandemic:
tracking technologies and immunity passports. The principal objective of the survey was to
understand which aspects of tracking policies are considered acceptable, and which might be
opposed because of their implications for privacy. A second objective of the survey was to
identify potential predictors of policy acceptance from a set of candidate attitude constructs,
such as political worldviews, trust in government, and perceived risk from COVID.
Tracking technologies
Several tracking “apps” exist for use in smartphones. A general property of all apps is that they
keep track of a person’s contacts with others, and if a person turns out to be infected, everyone
they encountered during the previous critical time period are identified and alerted via text
message. The apps differ, however, in where and how they store the contact information.
Some tracking apps involve central storage (e.g., on a government server), whereas others keep
all information local and communicate to contacts without that information being knowable
by health authorities or the government.
At the policy level in the United Kingdom, multiple developments and analyses have ulti-
mately led to the release of a decentralized tracking app in September 2020, the NHS COVID-
19 app. Although the Coronavirus Bill [3] passed on 25 March 2020 did not include any provi-
sions for wider surveillance tracing, public health enforcement in the U.K. has had existing
widespread power to request contact data for infectious or potentially infectious persons.
As early as March 2020, the Information Commissioners’ Office (ICO) opined that use of
mobile phone data would be legal [4] if broader contact-tracing were introduced (presumably
by legislation). The ICO also acknowledged that anonymous geolocation data is already being
used to fight the pandemic and approved its use [5].
Setting aside legal considerations, there is no doubt that all existing tracking technologies
come with wide-ranging implications for people’s privacy. The implications of “public-health
surveillance” have stimulated much concern among some scholars [6]. In the U.K., more than
170 cybersecurity and privacy experts signed an open letter in April 2020 [7], warning the gov-
ernment against use of a centralized tracking app for mass surveillance. (The first author of
this article was a signatory of that letter.) At the time, the U.K. government was eschewing a
decentralized approach that had been jointly developed by Apple and Google. Two months
later, the British government discontinued the centralized approach and switched to the
Apple/Google Bluetooth-based approach that is at the heart of the NHS COVID-19 app.
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Funding: This work was supported by the Elizabeth
Blackwell Institute, University of Bristol, with
funding from the University’s alumni and friends.
The first author was supported by a Humboldt
Award from the Humboldt Foundation in Germany
during part of this work.
Competing interests: The authors have declared
that no competing interests exist.
Notwithstanding the shift to a decentralized system, the number of downloads has fallen
short of the target required for effective control of the pandemic. As of late October 2020, only
half as many people have downloaded the app as needed to effectively halt the spread of the
virus [8]. One reason for the insufficient number of downloads may be the public’s concerns
about privacy. In most societies, people are known to place considerable value on their privacy.
A survey of the public in 27 countries within the European Union found that 87% of the public
found protection of their privacy to be important or very important [9]. Similarly, in a more
recent survey in Germany, 82% of respondents claimed that they are very or at least somewhat
concerned about their data privacy [10]. One might therefore expect the public to be con-
cerned about the invasion of privacy that is a nearly inevitable by-product of any COVID-
related tracking technology. What is unknown, however, is how specific features of candidate
technologies affect people’s attitudes, what safeguards might reduce privacy concerns, and
how political views and risk perceptions determine views on privacy-encroaching measures.
There are several reasons why privacy concerns might ultimately take a back seat in the
context of controlling COVID-19. First, people in the European Union generally endorse the
reuse of health data for the common good, although they are concerned about the exploitation
of that data through commercialization [11]. Second, there is evidence that people’s concerns
about privacy are highly context dependent [12]. Specifically, it has been proposed that people
engage in a “privacy calculus”, such that people will self-disclose personal information, for
example by using social media, so long as the perceived benefits exceed the perceived negative
consequences [13]. If people engage in a privacy calculus, the variables driving that calculus
must be examined and understood.
In the context of mobile apps, recent research has illustrated aspects of users’ privacy calcu-
lus. For example, one recent study showed that users’ privacy concerns were a function of sev-
eral variables, such as prior privacy experience (in particular violations of privacy), anxiety
about the role of computers and automation generally, perceived control over personal data,
and concerns about giving permission for an app to use personal data [14]. Although all those
variables significantly contributed to privacy concerns, the largest share of variance was
explained by the permission concern; that is, reluctance to accept an app’s requests to grant
access to personal data. Other research has explored the specifics of privacy permissions [15].
This study showed that providing justification for permission requests (e.g., explaining who
would have access to the data generated by the app) significantly reduced privacy concerns
whereas perceived sensitivity of the requested information enhanced privacy concerns. More-
over, perceived popularity of an app reduced privacy concerns and was directly linked to
greater download intentions.
These precedents suggest that users’ decisions about app download and usage are attuned
to relevant variables. The present context is, however, uniquely different from other apps
because tracking technologies do not deliver a direct benefit to users—on the contrary, being
notified of a contact with an infected person may benefit public health but the personal conse-
quences, such as self-isolation, are largely negative. Another difference to conventional apps is
that contact tracing works best if a large number of people are using the app. The privacy cal-
culus of contact-tracing apps is thus likely to involve a different set of variables, such as per-
ceived risk from COVID-19, that are not part of the typical repertoire of research on app usage
and privacy.
Immunity passports
Although at present immunity passports do not exist, they are within reach as serological tests
are becoming available, although to date their reliability has been insufficient [16]. The ethical
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implications of immunity passports are hotly debated in the literature [17–20]. The primary
concern involves the implications of immunity, which free the person from being subjected to
social distancing measures because they are presumed not to be infectious themselves. Con-
versely, people who are not immune may be confined to their homes and locked out of society
[18].
Historical precedent from the 19th century suggests that this division of society into those
who are immune and those who are not can have dystopian consequences—affecting all
aspects of life, from choice of job to choice of romantic partners [18]. In addition to stratifying
along a new dimension of biologically-determined “haves” and “have nots”, the mere existence
of immunity passports would also trigger an erosion of privacy because passports can only be
useful to the extent that their holders are monitored and checked. Passports may also ironically
create a risk to public health: If the privileges associated with immunity are sufficiently great,
there may be sufficient incentive for people to seek self-infection with the virus [18]. Some
scholars have therefore argued that “this idea has so many flaws that it is hard to know where
to begin” [18].
On the other side of the ledger, some scholars have argued that under certain circum-
stances, immunity passports may be ethical, provided sufficient safeguards are put in place [16,
17,19,20]. For example, it has been argued that certification of one’s immunity status may
spur people into greater prosocial altruism, for example by taking on riskier treatment roles or
donating blood [17]. Another suggestion has been to prioritize critical or high-risk sectors of
society (e.g., health care workers) for testing and to issue immunity passports only to people
within that sector, thus limiting inequities to people whose welfare is deemed to be of particu-
lar interest to society overall [16].
The present study
We now report the results from two large-scale surveys conducted in the United Kingdom
during the height of the pandemic (March-April 2020) that probed the public’s attitude
towards both privacy-encroaching options to combat the pandemic: tracking technologies and
immunity passports. The surveys presented people with one of several different hypothetical
scenarios that described a tracking app, accompanied by different policy options (e.g., a sunset
clause for data retention). We also collected a variety of attitude measures, such as people’s
worldviews, trust in government, and their risk perception relating to COVID, to identify
potential predictors of policy acceptance.
Method
Overview
The two survey waves were conducted roughly three weeks apart and were nearly identical,
with differences noted below. The preregistration for the first survey wave can be found at
https://osf.io/d3pcn. The second wave inherited the same preregistration. The surveys reported
here are part of a larger, international project that involved data collection in 7 countries
(U.K., Australia, U.S.A., Germany, Taiwan, Spain, and Switzerland). A continually-updated
summary of the overall project is available at https://stephanlewandowsky.github.io/
UKsocialLicence/index.html.
The first wave included two policy scenarios, each revolving around a different hypothetical
tracking app: In one scenario, the public had to opt in voluntarily and could choose whether
or not do download the app (called the “mild” scenario from here on). In the other scenario,
all mobile users were mandated to download the app and the government could issue
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quarantine orders and use the tracking data to locate and fine people who were violating those
orders (“severe” scenario).
The second wave additionally included a third hypothetical scenario (“Bluetooth”), in
which people’s phones exchanged messages anonymously whenever they were in proximity,
thus alerting people who may have been infected without the government knowing who they
are or where they were. Use of this app was voluntary.
The choice of those specific scenarios was based on discussion of apps that resembled or
instantiated those scenarios in the media and by government. However, at the time of the sur-
veys, no app was available in the U.K., with limited testing only commencing on 22 April.
After a switch in technology, a decentralized app relying on the Google/Apple Bluetooth tech-
nology became available for download by the public on 24 September 2020.
Participants
The first survey was conducted on 28 and 29 March 2020 and involved a representative sample
of 2,000 U.K. participants, recruited through the online platform Prolific (https://www.
prolific.co/). Prolific stratifies representative samples for age, sex and ethnicity. Participants
were at least 18 years old and were paid 85 Pence for their participation in the 10-minute
study. At the time, there were 14,543 confirmed cases of COVID-19 in the U.K., with 1,161
deaths.
The second wave was conducted on 16 April and involved another Prolific representative
sample of 1,500 participants. Participants were paid GBP 1.34 for their participation in the
(approximately) 15-minute study. This was equivalent to GBP 5.98 per hour based on the aver-
age observed completion time. At the time of the second wave, there were 98,476 confirmed
cases of COVID-19 in the U.K., with 14,915 deaths attributed to the disease. Notwithstanding
the relatively brief temporal window between waves, deaths increased 13-fold within less than
3 weeks.
Instrument and procedure
Verbatim copies of the surveys are available at https://osf.io/d3pcn (for the first wave) and
https://osf.io/pw5yj/ (for the second wave). Fig 1 provides an overview of the survey instru-
ment used in both waves. Each white box represents a block with one or more questions per-
taining to that topic or construct. Numbers next to the white boxes indicate the number of
items in that block. The black boxes represent the different tracking scenarios being tested.
Comprehension questions immediately after the scenario and a free text box at the very end of
survey (for additional comments) are not shown. The comprehension questions asked partici-
pants to state what the scenario they had just read was about. The correct answer (“The gov-
ernment considering using tracking technology to help reduce the spread of COVID-19”) had
to be selected from among foils such as “using new technology to eliminate influenza” or
“developing a vaccine to immunize the population from COVID-19.” Respondents who failed
the comprehension question were excluded from analysis. Unless otherwise noted, all survey
items were developed by the present authors.
Participants first responded to items that probed their perceived risk from COVID-19 itself.
Those items are shown in Table 1. The table displays the core question for each item: the exact
wordings differed slightly between waves and scenarios and are available in the full survey
texts. Those differences arose from inspection of the results from the first wave, and data gath-
ered in parallel surveys in other countries, which were carefully examined to identify possible
ambiguities or other problems with the items. All responses used a 5-point scale, where higher
values always corresponded to endorsement of the issue being probed (e.g., 1 = Not at all
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concerned to 5 = Extremely concerned). The labels and endpoints differed slightly between
items and are available in the full survey texts. Participants were then randomly assigned to
scenarios in each wave and were presented with the scenario text.
The text of the mild scenario was:
The COVID-19 pandemic has rapidly become a worldwide threat. Containing the virus’
spread is essential to minimise the impact on the healthcare system, the economy, and save
many lives. The U.K. Government might consider using smartphone tracking data to iden-
tify and contact those who may have been exposed to people with COVID-19. This would
help reduce community spread by identifying those most at risk and allowing health ser-
vices to be appropriately targeted. Only people who downloaded a government app and
agreed to be tracked and contacted would be included in the project. The more people
download and use this app, the more effectively the Government would be able to contain
the spread of COVID-19. Data would be stored in an encrypted format on a secure server
accessible only to the U.K. Government. Data would only be used to contact those who
might have been exposed to COVID-19.
The severe scenario was:
The COVID-19 pandemic has rapidly become a worldwide threat. Containing the virus’
spread is essential to minimise the impact on the healthcare system, the economy, and save
Fig 1. Overview of surveys used in both waves.
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Table 1. Items querying risks from COVID-19.
Question Label
How severe do you think novel coronavirus (COVID-19) will be for the general population? General harm
How harmful would it be for your health if you were to become infected COVID-19? Personal harm
How concerned are you that you might become infected with COVID-19? Concern self
How concerned are you that somebody you know might become infected with COVID-19? Concern others
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many lives. The U.K. Government might consider using phone tracking data supplied by
telecommunication companies to identify and contact those who may have been exposed to
people with COVID-19. This would help reduce community spread by identifying those
most at risk and allowing health services to be appropriately targeted. All people using a
mobile phone would be included in the project, with no possibility to opt-out. Data would
be stored in an encrypted format on a secure server accessible only to the U.K. Government
which may use the data to locate people who were violating lockdown orders and enforce
them with fines and arrests where necessary. Data would also be used to inform the appro-
priate public health response and to contact those who might have been exposed to
COVID-19. Individual quarantine orders could be made on the basis of this data.
The Bluetooth scenario, used in the second wave only, was:
The COVID-19 pandemic has rapidly become a worldwide threat. Containing the virus’
spread is essential to minimise the impact on the healthcare system, the economy, and save
many lives. Apple and Google have proposed adding a contact tracing capability to existing
smartphones to help inform people if they have been exposed to others with COVID-19.
This would help reduce community spread of COVID-19 by allowing people to voluntarily
self-isolate. When two people are near each other, their phones would connect via Blue-
tooth. If a person is later identified as being infected, the people they have been in close
proximity to are then notified without the government knowing who they are. The use of
this contact tracing capability would be completely voluntary. People who are notified
would not be informed who had tested positive.
People’s acceptability of the scenario was then probed 4 times (see left-most column in Fig
1). The initial test was immediately after presentation of the scenario. The wording of the
acceptability question differed between scenarios to reflect the attributes of the policy. For the
mild scenario, the question asked whether a participant “would download and use” the app,
whereas for the severe scenario the question was whether the “use of tracking data in this
scenario is acceptable.” For the Bluetooth scenario, the participant was asked whether they
“would use” the capability.
The second test occurred after a number of intervening questions that probed the perceived
benefits of the tracking app described in the scenario, as well as the risks and harms that could
arise from release of the personal data gathered in the process. Those items are shown in
Table 2. The table again displays the core question for each item, with the exact wording
available in the survey texts. All responses used a 6-point scale, where higher values always
corresponded to endorsement of the issue being probed by the item (e.g., 1 = Not at all to
6 = Extremely). Items of differing polarity are identified by “[R]” in the table and were reverse
scored before computing aggregate scores. For example, the items probing difficulty of declin-
ing participation and having control of their data are of different polarity because a consistent
position would imply endorsement of one and rejection of the other. The scale labels and end-
points differed slightly between items and are available in the full survey texts.
The second test of acceptability was immediately followed by two more items that again
queried acceptance of the scenario, but under modified assumptions. Those items were only
presented to participants who found the scenario unacceptable on the second occasion. The
first modification involved a sunset clause and queried whether deletion of the data after 6
months would make the scenario acceptable. The second modification differed betweeen sce-
narios and queried acceptability if the data were stored locally rather than on a government
server (mild) or if users could opt out of data collection (severe). This second modified
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assumption was not queried in the Bluetooth scenario (dotted empty field in the figure)
because it already involved local storage and voluntary participation.
Assessment of acceptability was followed by an assessment of people’s political worldviews,
using three items that probed endorsement of free markets and small government. Two of
those items were imported from previous research [21]; “An economic system based on free
markets unrestrained by government interference automatically works best to meet human
needs” and “The free market system may be efficient for resource allocation but it is limited in
its capacity to promote social justice”. The latter item was reverse coded for analysis. The third
item (“The government should interfere with the lives of citizens as little as possible”) was cre-
ated for the purposes of this study. Worldview was scored such that higher average responses
reflected more conservative-libertarian worldviews.
In the second wave, the worldview questions were preceded by a series of questions that
probed people’s attitudes to “immunity passports.” Immunity passports were explained as
follows:
An “immunity passport” indicates that you have had a disease and that you have the anti-
bodies for the virus causing that disease. Having the antibodies implies that you are now
immune and therefore unable to spread the virus to other people. Thus, if an antibody test
indicates that you have had the disease, you could be allocated an immunity passport which
would subsequently allow you to move around freely. Immunity passports have been pro-
posed as a potential step towards lifting movement restrictions during the COVID-19
pandemic.
Table 3 explains the items used to query attitudes towards immunity passports. The table
again displays the core question for each item, with the exact wording available in the survey
text for wave 2. Responses used a 5-point or 6-point scale, where higher values always
Table 2. Items querying potential benefits (Bfit) and harms (Harm) of app.
Item Question Label
Bfit 1 How confident are you that the Government app would reduce your likelihood of
contracting COVID-19?
Reduce
contracting
Bfit 2 How confident are you that the Government app would help you resume your normal
activities more rapidly?
Resume normal
Bfit 3 How confident are you that the Government app would reduce the spread of COVID-
19?
Reduce spread
Harm
1
How difficult is it for people to decline participation? Difficult decline
[R]
Harm
2
To what extent do people have ongoing control of their data? Have control
Harm
3
How sensitive is the data being collected? Sensitivity
Harm
4
How serious is the risk of harm from the proposed policy? Risk of tracking
Harm
5
How secure is the data that would be collected? Data security [R]
Harm
6
To what extent is the Government only collecting the data necessary to achieve the
purposes of the policy?
Proportionality
Harm
7
How much do you trust the Government to use the tracking data only to deal with the
COVID-19 pandemic?
Trust intentions
Harm
8
How much do you trust the Government to be able to ensure the privacy of each
individual?
Trust privacy
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corresponded to endorsement of the issue being probed (e.g., 1 = Not at all to 6 = Extremely).
The final column in the table indicates the scale (5 or 6 points) for each item. Items of different
polarity that required reverse scoring before being aggregated into a composite score are iden-
tified by “[R]”.
Altogether the surveys contained 32 (first wave) and 43 items (second wave), including one
attention filter presented immediately after the scenario that tested participants’ comprehen-
sion of the gist of each scenario. Because some questions were contingent on earlier responses,
not all participants saw all items. Not all items in the surveys are analyzed and reported here
although raw data for unreported items are made available at the link below.
Participants were invited to enter the survey through a link placed on Prolific. After reading
an information sheet that explained the study, participants were given the option to provide
consent and affirm that they were 18 or older via mouse click. Participants then responded to
the items in the sequence shown in Fig 1. The survey concluded with debriefing information
that included links to official websites with information about COVID-19 and resources for
assistance for anxiety or other mental health concerns relating to the pandemic.
Ethics statement
The study received ethics approval from the University of Bristol. Approval numbers 102663
(Wave 1) and 103344 (Wave 2). All participants gave informed consent and were fully
debriefed. The full text of the information and debriefing sheets is included with the surveys at
the links above.
Results
Data and source code availability
The data are available at https://osf.io/42wj6/ (first wave) and https://osf.io/pw5yj/ (second
wave). Demographics and other sensitive variables (such as location information) that could
lead to deanonymization have been omitted from the published data sets. The source code for
analysis is embedded in the report at https://stephanlewandowsky.github.io/UKsocialLicence/
index.html.
Data preparation and demographics of sample
The requested number of participants was not obtainable in either wave in a reasonable time.
The survey was discontinued after approximately 24 hours had elapsed since the last response
was collected, which yielded a sample of N= 1987 and N= 1493 for the first and second wave,
Table 3. Items querying attitudes towards immunity passports.
Question Label Scale
Would you support a government proposal to introduce immunity passports? Support 6
How concerned are you about the idea of introducing an immunity passport? Concern [R] 5
How much would you like to be allocated an immunity passport? Like self 6
To what extent do you believe an immunity passport could harm the social fabric? Harm general
[R]
6
Is it fair for people with immunity passports to go back to work, while individuals without
a passport cannot?
Fairness 6
To what extent would you consider infecting yourself with COVID-19 to get an immunity
passport?
Infect self 6
Would you support a government proposal to introduce immunity passports? Support 2 6
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respectively. After removal of duplicate responses from the same Prolific ID (N= 142 and
N= 1, respectively, for wave 1 and 2), participants who failed the comprehension check, and
incomplete responses, the final sample retained for analysis contained N= 1810 and N= 1446
for wave 1 and 2, respectively. The large number of duplicate responses in wave 1 arose from
the need to run the survey in two batches on consecutive days because Prolific does not permit
samples greater than 1,500. The second wave was run in a single batch but included a question
whether a participant had taken the survey before. 174 respondents indicated yes, and a further
131 were unsure. Given the 3-week lag between waves, all those responses were retained for
the second wave. Basic demographics are shown in Table 4 for both waves.
Self-reported education level is shown in Table 5. Samples from both waves were
remarkably similar, although in both instances the share of respondents who indicated that
they had a university education exceeded the official figure (latest release from Office of
National Statistics in 2017 indicates 42% graduates in population; https://www.ons.gov.uk/
employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/
graduatesintheuklabourmarket/2017). The difference may reflect the composition of the Pro-
lific sample (which is stratified by age, sex and ethnicity but not education) or it may reflect a
slight ambiguity in our question, which asked for the highest level of education completed, but
did not specify successful graduation as a criterion. Anyone having attended university would
therefore have been likely to choose “University education” from among the choices on offer.
Perceived risk from COVID-19
Fig 2 shows the distribution of responses to the 4 items querying people’s perceived risk from
COVID-19 for both waves. The items are explained in Table 1. The figure shows that there
were only small differences between the two waves. On both occasions, people expressed con-
siderably more concern for others than for themselves.
Attitudes towards tracking scenarios
The first part of the analysis focused on the various tracking policies sketched in the scenario
presented to participants.
Overall acceptability of scenarios. Fig 3 shows acceptability ratings for the first wave of
the survey, and Fig 4 shows the same ratings for the second wave. The four groups of bars in
each figure refer to the 4 occasions on which acceptability was queried in the survey (see Fig
1). The last two items were only presented to participants who found the scenario unacceptable
at the second test. The bars in the figure for those items represent the acceptable responses
Table 4. Demographics for both waves.
Gender Age
Wave Male Female Other Mean SD
Wave 1 48.8% 51% 0.1% 45.6 15.36
Wave 2 48.2% 51.7% 0.1% 46.15 15.32
https://doi.org/10.1371/journal.pone.0245740.t004
Table 5. Self-reported level of education for both waves.
Education
Wave GCSE A levels/VCE University Apprent/Vocatnl
Wave 1 15.3 17.3 55.6 11.7
Wave 2 14.6 17.2 56.4 NA
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Fig 2. Perceived risk from COVID during the first wave (28-29 March 2020; top set of panels) and during the
second wave (16 April 2020; bottom panels). Each panel plots responses for one item. See Table 1 for explanation of
the items.
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Fig 3. Acceptability of scenarios during the first wave (28-29 March 2020). The 4 pairs of bars refer to the 4
acceptability questions; see Fig 1. Error bars are 95% confidence intervals computed by R function prop.test.
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Fig 4. Acceptability of scenarios during the second wave (16 April 2020). The 4 pairs of bars refer to the 4
acceptability questions; see Fig 1. Error bars are 95% confidence intervals computed by R function prop.test. Note that
the “Sunset + other” question was not presented in the Bluetooth scenario.
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from the second test and the additional acceptance elicited by addition of a sunset clause and/
or local storage or opt-out.
Overall, acceptance of tracking technologies was quite high, with a baseline at the initial test
of around 70% for the mild and Bluetooth scenarios, and above 60% for the severe scenario.
To examine the effect of the different scenarios, a separate logistic regression was conducted
on data from each wave with the type of scenario entered as the only factor and the acceptance
response serving as binary dependent variable. For the first wave, the difference between sce-
narios in initial acceptance approached, but failed to reach, conventional significance levels,
b=−0.18, 95% CI [−0.38, 0.02], z=−1.79, p= .074. For the second wave, the mild and the
Bluetooth scenarios did not differ from each other, b= 0.03, 95% CI [−0.25, 0.30], z= 0.20, p=
.843, whereas the difference between the mild and the severe scenarios was significant, b=
−0.32, 95% CI [−0.59, −0.06], z=−2.36, p= .018.
Acceptance of all scenarios declined slightly at the second test, after participants responded
to more detailed questions about benefits and potential harms of the scenarios (reported next).
The addition of a sunset clause boosted acceptance for all scenarios, as did the provision of
local storage (for mild) and opt-out (severe). Indeed, with an opt-out clause, the severe sce-
nario achieved nearly 90% acceptance. It must be borne in mind, however, that the severe sce-
nario with an opt-out clause closely resembles the mild scenario, the remaining difference
being that the mild scenario requires opt-in rather than permitting opt-out.
Potential effectiveness of tracking. Fig 5 shows responses from both waves to three ques-
tions about the perceived benefits of the tracking scenario. The panels and Y-axes use the labels
from Table 2. It is clear that the severe scenario is judged to be slightly, but not dramatically,
more effective than the other two for at least some of the items. The two waves do not appear
to differ appreciably from each other.
Separate one-way ANOVAs for each wave and item confirm the pattern in the figure, with
an effect of scenario type on Reduce Contracting in wave 1, F(1, 1808) = 5.60, MSE = 1.88, p=
.018, ^
Z2
G¼:003, and in wave 2, F(2, 1443) = 13.13, MSE = 1.75, p<.001, ^
Z2
G¼:018. Follow-
up comparisons by Tukey HSD revealed that the severe scenario differed from each of the
others in wave 2, whereas the mild and Bluetooth scenarios did not differ from each other.
For Resume Normal, scenario type had no effect in wave 1, F(1, 1808) = 0.84, MSE = 1.93, p=
.360, ^
Z2
G¼:000, but it did have an effect in wave 2, F(2, 1443) = 5.01, MSE = 1.81, p= .007,
^
Z2
G¼:007, which arose from a significant difference between the severe and Bluetooth scenar-
ios. No other pairwise tests were significant. Finally, for Reduce Spread, scenarios differed sig-
nificantly in wave 1, F(1, 1808) = 5.63, MSE = 1.91, p= .018, ^
Z2
G¼:003, and wave 2, F(2, 1443)
= 8.06, MSE = 1.70, p<.001, ^
Z2
G¼:011, with the difference in wave 2 arising from the severe
scenario leading to significantly higher confidence than the other two scenarios, which did not
differ from each other.
Potential harms from tracking. The items probing harm (Table 2) were aggregated into
three clusters that represented, respectively, people’s perceived control over the policy (Items
Difficult decline [R] and Have control), harms from the policy (Sensitivity, Risk of tracking,
Data security [R]), and trust in government (Proportionality, Trust intentions, Trust privacy).
Items within each cluster were averaged after reverse scoring where appropriate (items identi-
fied with [R]).
Fig 6 shows responses from both waves to the three clusters of questions about the per-
ceived harms of the different tracking technologies. Each panel presents the composite score
for each cluster of items.
Separate one-way ANOVAs were conducted for each wave and item cluster where appro-
priate. For the control cluster, the severe scenario was omitted from any analysis because—
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Fig 5. Participants’ confidence in the expected benefits from the three tacking policies. Box plots enclose 95%
confidence intervals. Panels represent different items, using the labeling from Table 2.
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Fig 6. Participants’ views on the expected harms and risks of the three tacking policies and perceived trust in the
government’s intentions. Box plots enclose 95% confidence intervals. Panels represent different item clusters; see text
for details.
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quite appropriately—most people recognized that it offered no control at all. The only
ANOVA for the control cluster thus involved a comparison between the mild scenario and
Bluetooth in wave 2, F(1, 958) = 1.80, MSE = 0.76, p= .180, ^
Z2
G¼:002, which revealed that the
two scenarios did not differ from each other. For harms, differences between scenarios were
significant for wave 1, F(1, 1808) = 37.49, MSE = 1.12, p<.001, ^Z2
G¼:020, as well as for wave
2, F(1, 1808) = 37.49, MSE = 1.12, p<.001, ^Z2
G¼:020, with the latter effect being exclusively
driven by a difference between the severe and mild scenarios. For trust, effects were observed
in wave 1, F(1, 1808) = 20.96, MSE = 1.90, p<.001, ^
Z2
G¼:011, and wave 2, F(2, 1443) = 9.62,
MSE = 1.68, p<.001, ^
Z2
G¼:013, with the latter effect being driven by significant differences
between the mild scenario and each of the other two.
Predictors of tracking policy acceptance. The final analysis modeled acceptance of the
various policy scenarios as a function of several sets of predictors using logistic regression. The
predictors included wave (first or second), demographics (age and gender, excluding respon-
dents who did not choose “male” or “female”; N= 6), a measure of worldview aggregated
across the three relevant items; perceived risk from COVID (aggregated score across items in
Table 1); and the earlier aggregate scores for perceived harm from the policy and trust in gov-
ernment (top two panels of Fig 6; the bottom panel relating to control was omitted because
there was no meaningful variance for the severe scenario). The initial acceptance of the sce-
nario (i.e., the first time acceptance was probed) was used as the binary dependent variable.
We first attempted to fit a number of random effects models (e.g., with a different intercept
for each participant) using the lmer function in R. All of these models failed to converge. The
likely reason for this failure was near-zero variance of the random effect. We therefore fit a
conventional logistic regression using glm in R with fixed effects only.
We compared two fixed-effect models: a complex model which included the above predic-
tors and their interactions with wave, and a simpler model without any interaction terms.
Removing the interactions incurred no significant loss of fit, χ
2
(6) = 0.414, p >.10. Moreover,
the simpler model was preferred by BIC (2796 vs. 2845). We therefore only report the simpler
model with wave functioning as a predictor but not interacting with any of the others. Fig 7
shows the estimated standardized regression coefficients for the final model (intercept
omitted from figure). The model accounted for a substantial share of the variance, McFadden’s
pseudo-r
2
= 0.33 and Cragg and Uhler’s pseudo-r
2
= 0.48.
The figure shows that increasing age was associated with reduced acceptance of a policy,
and that men were less accepting than women overall. In addition, worldview had a small but
consistent effect on policy acceptance, such that people with a more conservative or libertarian
orientation were slightly less likely to accept any of the policies. Reduced acceptance was also
associated with greater perceived harms from the tracking technologies. Conversely, greater
perceived risk from COVID was associated with greater policy acceptance. Finally, by far the
most important predictor turned out to be trust in government. People who trusted the gov-
ernment to safeguard privacy were considerably more likely to accept the policies than people
who distrusted the government.
Immunity passports
The second part of the analysis focused on attitudes towards immunity passports. This analysis
is confined to wave 2 because questions about passports were limited to that wave.
Acceptance of immunity passports. Fig 8 displays the distribution of responses to all
items querying immunity passports. The majority of people clearly did not object to the idea
of passports, with concern being low on average and more than 60% of people wanting one
for themselves to varying extents. There were, however, around 20% of respondents who
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considered passports to be unfair and who opposed them completely (response category 1 for
the second support item).
Predictors of immunity passport acceptance. To model acceptance of immunity pass-
ports we created a composite score for immunity passports by averaging across all items in
Table 3, reverse scoring where necessary. The “Infect self” item was excluded because it exhib-
ited little variance and also did not correlate appreciably with most of the other items. The
composite score was used as the dependent variable in a linear regression model that included
most of the predictors from before; namely, age, gender (again excluding responses other
than “male” or “female”), worldviews, perceived risk from COVID-19, and the perceived
harms of the tracking policy and trust in government relating to that policy. Note that the latter
two measures were gathered in connection with the tracking policy rather than immunity
passports.
Fig 9 shows the regression coefficients for this model. The model accounted for a moderate
share of variance, r
2
= 0.17, adjusted r
2
= 0.17. Most of the variance accounted for was due to
the two predictors relating to the tracking scenario (Perceived harms and Trust in govern-
ment). When they were removed from the model, the explained variance was small, r
2
= 0.03,
Fig 7. Estimated standardized coefficients for a logistic regression to predict initial policy acceptance of the
tracking scenarios. Distributions span 95% confidence intervals. Horizontal bars span 90% confidence intervals.
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adjusted r
2
= 0.02, although age retained its significant effect, t(1439) = 4.16, p<.001, and the
effect of perceived risk from COVID-19 was now highly significant, t(1439) = 3.31, p= .001.
In contrast to the tracking policies, increasing age was associated with increased acceptance
of immunity passports whereas gender had no effect here. The other predictors relating to
Fig 8. Responses to items concerning immunity passports during wave 2. Each panel plots responses for one item
before reverse scoring. See Table 3 for explanation of the items. The “concern” item used a 5-point scale.
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perceived risks predicted attitudes towards immunity passports as one might expect: greater
perceived risk of the disease and greater trust in government were associated with more favor-
able attitudes whereas skepticism towards the tracking policies was also associated with greater
skepticism towards immunity passports.
Discussion
Potential limitations
Our surveys only included three different tracking scenarios, which is a small number relative
to the total number of technological solutions now available [22]. We also presented scenarios
that differed somewhat from those currently in use around the world. This was unavoidable
given that not many mature technologies existed at the time of our survey. Nonetheless, the
essence of the Bluetooth scenario described in wave 2 has now become a reality in many coun-
tries, including the United Kingdom.
Fig 9. Estimated standardized coefficients for a linear regression to predict favourable attitudes towards
immunity passports. Distributions span 95% confidence intervals. Horizontal bars span 90% confidence intervals.
Note that Trust in government and Perceived harms are obtained in response to the tracking policy scenario, not
immunity passports.
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In light of the relatively slight differences in acceptance between scenarios, it might be
argued that the participants did not fully understand the implications of the different hypo-
thetical tracking apps. We do not believe that this argument has much force: As shown in Fig 6
(bottom panel), people are very aware of how much control they have over their data in the dif-
ferent scenarios. Virtually all participants recognized, for example, that the severe scenario
offers no control over people’s personal data. In contrast, participants correctly recognized
greater control in the mild and Bluetooth scenarios. These results are not easily reconciled
with the proposition that participants failed to understand the scenarios.
Relationship to previous results
Our results mesh extremely well with those reported by an independent team of authors in the
U.K. [23]. Similar to both our survey waves, [23] found that up to 75% of respondents would
install the app. One difference between the studies is that we specified data storage and access
precisely, whereas [23] did not explicitly explain where the data would be held (i.e., locally
without location information or centrally). Their scenario was, however, closest to our Blue-
tooth scenario, which renders the two sets of acceptance probabilities nearly identical. The
present data are also consonant with recent results from Poland [24]. People in Poland, polled
in late March 2020, were also sensitive to the risk from COVID-19, with greater perceived risk
being associated with greater endorsement of tracking technologies. The role of political views
was more nuanced: Wnuk et al. found that people higher in right-wing authoritarianism were
more likely to endorse tracking technologies, whereas endorsement of liberty (the single item
“The freedom to do what we want is more important than following the recommendations of
the authorities”) was negatively associated with acceptance of tracking technologies.
Our results are somewhat more at variance with another recent report on app acceptance in
the U.K. [25]. That study recruited a very large sample (N ’12,500) in mid-May 2020 through
the Care Information Exchange (CIE), a patient-facing NHS web platform. Around 60% of
participants indicated willingness to download a contact-tracing app, although the study did
not elaborate on the app other than to describe it as “an NHS app for your phone (like the one
being tested on the Isle of Wight).” The majority of participants who declined participation
(67% of that group) indicated that their refusal was due to privacy concerns. There are several
possible reasons for the greater rates of endorsement observed here compared to [25]. In addi-
tion to obvious differences between samples (representative vs. self-selected users of an NHS
website), we explained our scenarios in greater depth, and the additional information may
have swayed people because it explained the public-health benefit of the technology.
There are also similarities between the two sets of results. Similar to us, [25] found that
advanced age (i.e., 80 and above) was associated with decreased likelihood of participation. In
addition, a reduced understanding of government rules and advice on the lockdown at the
time was associated with reduced willingness to download the app. Moreover, people who
thought they had COVID-19 and had recovered from it (without however being tested at any
point), were 27% less likely to download the app. This underscores the important role played
by variables that, at first glance, appear extraneous to a decision about app, but create the criti-
cally-important context for people’s acceptance of tracking technologies.
We are not aware of any existing data on people’s acceptance of immunity passports.
Implications for policy
Our results have clear implications for policy. Perhaps the most important finding is the high
overall level of endorsement for both policy options: A majority of people supports immunity
passports, and an even greater majority endorses tracking-based policies. This high level of
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endorsement stands in contrast to people’s commonly professed concern for their privacy
[26]. It appears that the British public is prepared to sacrifice some privacy in the interest of
public health, in particular when the scenarios are explained in detail (compare our results to
those of [25]).
A second implication of our results is that the fundamentals of the policy mattered relatively
little: In both waves, the initial acceptance of the mild scenario was only 5%—10% greater than
acceptance of the draconian severe scenario. This small difference is surprising in light of peo-
ple’s responses to opinion surveys which place a high value on privacy [27]. The small differ-
ence is, however, consonant with the fact that people tend to reveal personal information for
relatively small rewards, contrary to their stated opinion [26–28]. Participants were nonethe-
less quite sensitive to other aspects of the scenarios, as revealed by the relatively large effect of
the addition of a sunset clause or other measures such as local storage of the data.
Our results also reveal people to engage in a readily-understandable privacy calculus. Spe-
cifically, people trade off the perceived harms from the policy under consideration (tracking
apps or immunity passports) against the perceived risk from COVID-19: increased risk per-
ception increases policy acceptance and increased fear of fallout from the policies reduces
support. The magnitude of the opposing effects of those two variables was roughly equal for
tracking technologies, whereas for immunity passports, perceived harm outweighed perceived
risk of COVID-19. Similar tradeoffs between opposing risks and benefits have been observed
previously in the context of other mobile applications [29]. The most important driver of
acceptance of both policies was a variable unrelated to perceptions of immediate risks and
harms; namely, people’s trust in the government’s intention or ability to secure people’s pri-
vacy and to manage access to the data.
A further important aspect of our result is that neither tracking technologies nor immunity
passports appeared to be highly politicized, at least at the time the surveys were conducted.
Although people with a conservative-libertarian worldview were less likely to accept tracking
technologies (while being slightly more likely to endorse immunity passports), the effect sizes
were modest relative to the other variables that were observed to enter into the privacy calculus
here.
These implications can be combined into a straightforward policy for tracking apps: People
are relatively unconcerned about where the data are stored, but they do care about how long
the data will be stored for. Any policy should therefore be accompanied by a clear sunset
clause, and that sunset clause should be highlighted in communications with the public. In
addition, given the important role of trust, any policy rollout should be accompanied by clear
messages about why and how the government is a trustworthy custodian of people’s data.
Those messages must also be easy to understand and follow [25]. This may require the govern-
ment to explicitly step aside and highlight the fact that the data will be held by trusted institu-
tions, such as the NHS.
The implications for immunity passports are somewhat more nuanced. Although there was
a large degree of support for passports overall, one fifth of our sample strictly opposed their
introduction. It is an open question whether this opposition would create an insurmountable
political challenge to any government trying to introduce immunity passports.
Author Contributions
Conceptualization: Stephan Lewandowsky, Simon Dennis, Andrew Perfors, Yoshihisa
Kashima, Daniel R. Little.
Data curation: Stephan Lewandowsky.
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Formal analysis: Stephan Lewandowsky, Paul Garrett, Daniel R. Little.
Funding acquisition: Stephan Lewandowsky.
Methodology: Stephan Lewandowsky, Simon Dennis, Joshua P. White, Daniel R. Little, Muh-
sin Yesilada.
Project administration: Stephan Lewandowsky.
Software: Joshua P. White.
Visualization: Stephan Lewandowsky, Paul Garrett.
Writing – original draft: Stephan Lewandowsky.
Writing – review & editing: Stephan Lewandowsky, Simon Dennis, Andrew Perfors, Yoshi-
hisa Kashima, Paul Garrett, Daniel R. Little.
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