FigureRegional differences across the UK in mobility since the implementation of physical distancing
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 reductions 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. 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. 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. 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 prevent 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.
Editorial note: the Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations.
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