Docherty, Annemarie B

Drake, Thomas M

Harrison, Ewen M

Sheikh, Aziz

Weiser, Thomas G

Yule, Steven

English

In an attempt to reduce the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), an estimated 4·5 billion people globally have been placed under some form of lockdown restriction. In the UK, non-essential movement constraints and physical distancing have been in place since March 23, 2020. Modelling and early empirical investigations indicate that physical distancing measures are crucial to reduce transmission of the virus, consequent pressure on health systems, and the number of deaths. For these policies to be successful, adequate compliance must be achieved. To help monitor this compliance, we have developed code to enable us to map national mobility data for the UK, which can also be applied to the data available for 131 other countries and 1167 subregions. The code we used is openly available online

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     Edinburgh Research Explorer                                      The effects of physical distancing on population mobility duringthe COVID-19 pandemic in the UKCitation for published version:Drake, TM, Docherty, AB, Weiser, TG, Yule, S, Sheikh, A & Harrison, EM 2020, 'The effects of physicaldistancing on population mobility during the COVID-19 pandemic in the UK', The Lancet Digital Health, vol.2, no. 8, pp. E385-E387. https://doi.org/10.1016/S2589-7500(20)30134-5Digital Object Identifier (DOI):10.1016/S2589-7500(20)30134-5Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Publisher's PDF, also known as Version of recordPublished In:The Lancet Digital HealthGeneral rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 23. Feb. 2022  Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website.  Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.  Commentwww.thelancet.com/digital-health   Published online June 12, 2020  https://doi.org/10.1016/S2589-7500(20)30134-5 1For Apple’s Mobility Trends Reports see https://www.apple.com/covid19/mobilityThe effects of physical distancing on population mobility during the COVID-19 pandemic in the UKIn an attempt to reduce the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), an estimated 4·5 billion people globally have been placed under some form of lockdown restriction. In the UK, non-essential movement constraints and physical distancing have been in place since March 23, 2020. Modelling and early empirical investigations indicate that physical distancing measures are crucial to reduce transmission of the virus, consequent pressure on health systems, and the number of deaths. For these policies to be successful, adequate compliance must be achieved. To help monitor this compliance, we have developed code to enable us to map national mobility data for the UK, which can also be applied to the data available for 131 other countries and 1167 subregions. The code we used is openly available online.Adherence to movement restriction regulations is challenging and a risk of increasing non-compliance (or so-called lockdown fatigue) exists. Observational evidence suggests that rather than fatigue, the practicalities of daily living are leading some people in the UK to increase activity as their supplies decrease.1,2 These observations are important because a key modelling study of mitigation strategies for reducing transmission in the UK assumed a 75% reduction in contacts as part of a social distancing strategy that was subsequently implemented;3 whether this level of reduction is being achieved remains unclear.On March 29, 2020, Google released mobility data, aggregated from mobile device location information covering the period Feb 16 to March 29, 2020.4 These data can be used to infer the locations visited by an individual and to compare pre-restriction (baseline) to post-restriction activity. Data can be downloaded from Google as a comma separated value (CSV) file and are updated daily. We used these data because the Google Android operating system has the largest market share, and therefore is likely to capture the movements of more individuals than any other provider. Other providers have started to publish mobility data too, including Apple, although these data do not provide information on the type of mobility (ie, residential, in a park, or a workplace).We used Google mobility data to map spatial and temporal changes in mobility across the UK in six areas: residential areas; supermarkets, grocery shops, and pharmacies; workplaces; retail and recreational areas; transit stations (subway, bus, and train stations); and parks (figure). Comparing data for March 29, 2020, with baseline data from Jan 3 to Feb 6, 2020, we saw a 63% overall reduction in movement, with retail and recreational areas (decreased by 85%; not surprising given restrictions imposed on this sector) and transit stations (decreased by 75%) showing the largest reductions. Park use initially decreased but has now increased to levels seen before the lockdown restrictions, perhaps because of good weather or people adapting their exercise requirements. However, when we viewed data up to the week of May 2, we saw a slight increase in mobility across retail and recreational areas and transit, suggesting increased movement over time.Subsequent releases of data have allowed us to make comparisons of movement over the course of lockdown. Across the UK, non-residential and non-park movement has increased by 5% since lockdown began, increasing by 2–3% per week. Use of parks has increased since the first week of lockdown restrictions, which is to be expected, but remains 30% below the usual levels of mobility. Comparing movement on April 11 to that on April 5 and March 29, we saw no large changes in mobility and no clear trend towards increased mobility. However, by mapping these data we observe increases in mobility in specific regions, particularly the Midlands, north-east England, and Wales, where transit has increased by up to 25%. These findings show how data can provide information on differential adherence to movement restrictions geographically and over time. Dynamic spatial or temporal information could help guide the so-called exit strategy from current lockdown restrictions. Furthermore, combining data such as these with health-care information, such as COVID-19 testing results—with adequate privacy protections—could allow governments to develop localised movement policies.Interpreting these data poses challenges because they might not reflect differences in population density. For instance, rural areas might appear to have smaller Lancet Digital Health 2020Published Online June 12, 2020 https://doi.org/10.1016/S2589-7500(20)30134-5For Google COVID-19 Community Mobility Reports see https://www.google.com/covid19/mobility/For code used see https://github.com/SurgicalInformatics/covid-mobility-tracker/Comment2 www.thelancet.com/digital-health   Published online June 12, 2020  https://doi.org/10.1016/S2589-7500(20)30134-5reductions in mobility due to a requirement to travel further for supplies and the differing nature of rural occupations, such as farming, where mobility in the workplace is essential. Similarly, in dense population centres, larger reductions in mobility might be required to reduce transmission effectively. For instance, for a large transport hub that process tens of thousands of passengers each day, a 50% reduction in mobility might be insufficient to reduce disease transmission due the inability for adequate physical distancing in that environment. Hence, contextualising these data is essential for fair interpretation. Demographic differences in populations might also affect these data, with some groups (including older people and those in poverty) being less likely to own a mobile phone.5 Hence, mobility in these populations might not be captured and given the poorer health outcomes from COVID-19 in some subgroups, this is a key drawback for any mobility data that relies on consumer technology.Monitoring the effect of lockdown policies is crucial for updating model predictions to inform health-care system response. With more time spent under lockdown restrictions, cognitive errors such as confirmation bias (interpreting information in a way to support the aim to get back to normal) and optimism bias (ie, the opinion that “it won’t happen to me”) might weaken resolve for physical distancing, especially when worst-case predictions are not realised.6,7 Maintaining the behavioural changes necessitated for long periods of lockdown restriction requires a compelling narrative that addresses individual needs for autonomy, connection, and competence. Although not of our choosing, the population is learning a new skill (staying at home), and feedback is required to internalise external drivers into intrinsic motivators. Messages of thanks and approval for doing the right thing, combined with regional data on movement and infection rates can reinforce motivations to stay at home.Concerns about the ethical use of these data are important, both now and in the future. Many individuals are not fully aware of the scale and fidelity of information collected about them. Appropriate consent, options for opting out, and stringent privacy measures are essential if public trust in this information is to be maintained.4 Publishing these data in an anonymised and aggregated format is important to protect the privacy, safety, and trust of individuals, which must be considered to Figure: Regional differences across the UK in mobility since the implementation of physical distancingChange in activity from baselineMarch 29 vs Jan 3–Feb 6, 2020Most reduction Least reduction–100% –75% –50% –25% 0%Parks Workplace Retail and recreation Transit stationsChange in activity during lockdownApril 15 vs March 29, 2020No increased activity Increased activity0% 50%ParksParks, beaches, and public gardensWorkplacePlaces of workRetail and recreationRestaurants, cafes, shopping centres, museums, libraries, cinema (not pharmacies and supermarkets or grocery shops)Transit stationsPublic transport hubs such as bus, train, and subwayor tube stationsChange in activity during lockdownMay 2 vs April 15, 2020No increased activity Increased activity0% 50%Parks Workplace Retail and recreation Transit stationsCommentwww.thelancet.com/digital-health   Published online June 12, 2020  https://doi.org/10.1016/S2589-7500(20)30134-5 3prevent unintended consequences, such as victimising disadvantaged groups who are less able to practice physical distancing, discrimination, or even causing targeted law enforcement against these populations.AS is supported by Health Data Research UK. AS is a member of the Scottish Government’s COVID-19 Chief Medical Officer’s Advisory Group. This Comment in no way represents the views of the Scottish Government. SY reports consulting fees as a Member of the Global Education Council, Johnson & Johnson Institute. All other authors declare no competing interests. No institutional review board  or ethical approval was required for this analysis because no patient data were used. Similarly, the mobile user data were anonymised and ethical approval was not required.Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Editorial note: the Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations.Thomas M Drake, Annemarie B Docherty, Thomas G Weiser, Steven Yule, Aziz Sheikh, *Ewen M Harrisonewen.harrison@ed.ac.ukCentre for Medical Informatics, Usher Institute (TMD, ABD, AS, EMH), and Department of Clinical Surgery (TGW, SY), University of Edinburgh, Edinburgh, EH16 4UX, UK; Department of Surgery, Stanford University, Stanford, CA, USA (TGW); and Department of Surgery, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA (SY)1 CPG FMCG & Retail. COVID 19 intelligence round-up. Nielsen, March 3, 2020. https://www.nielsen.com/eu/en/insights/video/2020/covid-19-intelligence-roundup/ (accessed March 30, 2020).2 Rigby C. RetailX tracker: how has the way we shop changed, two weeks into coronavirus? Internet Retailing, March 27, 2020. https://internetretailing.net/covid-19/retailx-tracker-how-has-the-way-we-shop-changed-two-weeks-into-coronavirus-21175 (accessed March 27, 2020).3 Ferguson NM, Laydon D, Nedjati-Gilani G, et al. Report 9 - impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. London: MRC Centre for Global Infectious Disease Analysis, Imperial College, 2020: 1–20.4 Aktay A, Bavadekar S, Cossoul G, et al. Google COVID-19 Community Mobility Reports: anonymization process description (version 1.0). aRxiv.org: April 8, 2020. https://arxiv.org/abs/2004.04145 (accessed May 29, 2020). 5 Statista. Percentage of households with a mobile phone in the United Kingdom (UK) in 2018, by gross income group. 2018. https://www.statista.com/statistics/289305/distribution-of-mobile-phones-in-the-uk-by-income-group/ (accessed June 8, 2020).6 Garcia-Alamino J. Human biases and the SARS-CoV-2 pandemic. Intensive Crit Care Nurs 2020; 58: 102861.7 Helweg-Larsen M, Shepperd JA. Do moderators of the optimistic bias affect personal or target risk estimates? A review of the literature. Personal Soc Psychol Rev 2001; 5: 74–95.

The effects of physical distancing on population mobility during the COVID-19 pandemic in the UK

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The effects of physical distancing on population mobility during the COVID-19 pandemic in the UK

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