There is a critical need to understand the impact of early countermeasures intended to restrict mobility during epidemics. Our analyses used one publicly available measure, from Citymapper, to capture changes in mobility in 41 cities worldwide and relate them to the imposition of pandemic countermeasures. Our findings demonstrate that several policy restrictions, notably closures of public transport, workplaces and schools, had a substantial impact on reducing population mobility. Our finding of no such impact of fiscal or monetary measures (which were hypothesised not to have mobility impacts) adds to the specificity and therefore the plausibility of our findings.(15)
Strengths and weaknesses of the study
Our study has several important limitations. Most importantly, the data published by Citymapper are generated from within what is essentially a black box. The company has, however, given us some additional information and, following our request, published some of this on their website. Our robustness checks did, however, show that, at least in two cities, the changes observed correlated with those found using other data sources.
Second, there may be specific local factors that would need to be considered in interpreting the data. For example, mobility patterns in Hong Kong may have been reduced over the past year due to the pro-democracy demonstrations. Third, Citymapper has only published its Mobility Index since the beginning of the pandemic and, while there is now more data available since the time of original writing, we sought to focus on the initial, comparative stages of the epidemic where critical variations in country policy responses took place. Fourth, although the data include cities from across the world, there are some notable gaps, such as mainland China and India. Fifth, as the data are user-generated, there may be selection bias due to the characteristics of those using the app, especially because the app is less widely used for private transportation. More specifically, the sample might over represent those individuals using public transportation compared with those using privately owned cars.Another study using mobile phone data showed how, in New York City, there was a substantially greater reduction in mobility among residents of wealthy areas than poor ones, reflecting the greater ability of the former to work from home.(16) Moreover, given that some measures were introduced at later stages in the pandemic in each country, our models may underestimate the full effect of those measures as more people were already subject to restrictions. More recent research on the effect of countermeasures on transmission of the virus can take advantage of emerging individual-level data to address this issue (17).
Finally, while mobility is a reasonable proxy for contact rates, our study has not attempted to model the subsequent impact of mobility restrictions on incidence or transmission of COVID-19. There is some evidence, for example, that in the case of COVID-19 and other novel corona viruses, school closures may have smaller impacts on transmission than is the case with established influenza virus infections.(18) Future research is needed to operationalise mobility measures as a parameter of contact rates in standard susceptible-exposed-infected-recovered (SEIR) or SEAIR (susceptible, exposed, asymptomatic, infectious, removed) epidemiological models of COVID-19 spread, with findings feeding into models of the impact of non-pharmaceutical interventions, such as that developed by Imperial College.(19)
Citymapper is only one source of and, as the pandemic progresses, new sources are coming online, although often not in ways that make it easy to conduct analsyes such as the ones we conducted. Thus, Google has published a series of COVID-19 Community Mobility Reports but the graphs are in pdf format and it is not clear that the data are available.(20) The Citymapper data seem, as far as we can ascertain, to be unique in being in the public domain and covering cities worldwide. This makes it possible to do the analyses we have performed and thus understand the consequences of different policies. This is important given the high social costs of these policies, which will inevitably weigh upon politicians called on to make difficult choices.
Meaning of the study
Our findings suggest that policies to restrict movement are essential for rapid and dramatic reductions in population mobility. This is evidenced, for example, in the steep decline in mobility in the UK after implementation of restrictions, compared with the more modest previous reductions following government advised but not mandated behaviour change. Our data cover the period of initial implementation of restriction policies; questions remain about whether coercive policies are sustainable over long periods, particularly if measures are perceived as socio-economically inequitable.(21)
Our analyses suggest that closure of public transport, workplaces and schools achieved substantial reductions in mobility. However, further work is needed to examine how and if these policies could generate unintended consequences including: undermining health systems and other essential services, due to staff’s loss of transport or childcare; economic hardship arising from loss of earning; and increasing children’s social contact with grandparents.(22) Given these considerations, it is likely that politicians will look to pragmatic policies, such as: reducing but not closing public transport services; workplaces and schools remaining open in the case, respectively, of essential services and the children of key workers;(23) and rapid and generous income maintenance programmes. Lifting too many measures at once without appropriate surveillance and safeguards in place may cause a rapid resurgence of transmission, as is already being seen in some American states. However, monitoring mobility changes can inform continuous assessments of policy impact.
Unsurprisingly, there is relatively little other research with which to compare these findings. Exceptions include recent reviews of the effect of school closures on disease transmission, finding that this measure has some but not a great impact,(18) reducing social contact among students but not to zero and possibly with unintended consequences for mixing across schools and across generations
Comparing cities in different regions, it does appear that there have been large reductions in mobility across most of Europe, perhaps to a greater degree than was anticipated by policymakers. There is a somewhat different pattern in Asia, where the focus has been much more on case ascertainment, contact tracing and isolation, making use of the capacity to undertake widespread testing. However, it is notable that Singapore, where the reduction in mobility was least, is now implementing restrictions that it had previously avoided.(24)
Conclusion and Policy Implications
In a world where large numbers of people carry with them devices with what would, until recently, have been considered impossible amounts of computing power, there are many new opportunities open to epidemiologists which, as in this case, can provide new insights into the impact of policy, providing evidence that can be used for safeguarding health and well-being. Very recent work uses phone data to track changes in mobility, which could ultimately be used to obtain more insights on contact rates (17, 25). Yet, it is also important to remember that such information can be used for other purposes, raising concerns about privacy, and it will always be necessary to balance the opportunities and the threats of the digital environment.(26)