Using volunteered geographic information to assess mobility in the early phases of the COVID-19 pandemic: a cross-city time series analysis of 41 cities in 22 countries from March 2nd to 26th 2020

Abstract

Objectives Restricting mobility is a central aim for lowering contact rates and preventing COVID-19 transmission. Yet the impact on mobility of different non-pharmaceutical countermeasures in the earlier stages of the pandemic is not well-understood. Design Trends were evaluated using Citymapper’s mobility index covering 2nd to 26th March 2020, expressed as percentages of typical usage periods from 0% as the lowest and 100% as normal. China and India were not covered. Multivariate fixed effects models were used to estimate the association of policies restricting movement on mobility before and after their introduction. Policy restrictions were assessed using the Oxford COVID-19 Government Response Stringency Index as well as measures coding the timing and degree of school and workplace closures, transport restrictions, and cancellation of mass gatherings. Setting 41 cities worldwide. Main outcome measures Citymapper’s mobility index. Results Mobility declined in all major cities throughout March. Larger declines were seen in European than Asian cities. The COVID-19 Government Response Stringency Index was strongly associated with declines in mobility (r = − 0.75, p < 0.001). After adjusting for time-trends, we observed that implementing non-pharmaceutical countermeasures was associated with a decline of mobility of 10.0% for school closures (95% CI: 4.36 to 15.7%), 15.0% for workplace closures (95% CI: 10.2 to 19.8%), 7.09% for cancelling public events (95% CI: 1.98 to 12.2%), 18.0% for closing public transport (95% CI: 6.74 to 29.2%), 13.3% for restricting internal movements (95% CI: 8.85 to 17.8%) and 5.30% for international travel controls (95% CI: 1.69 to 8.90). In contrast, as expected, there was no association between population mobility changes and fiscal or monetary measures or emergency healthcare investment. Conclusions Understanding the effect of public policy on mobility in the early stages is crucial to slowing and reducing COVID-19 transmission. By using Citymapper’s mobility index, this work provides the first evidence about trends in mobility and the impacts of different policy interventions, suggesting that closure of public transport, workplaces and schools are particularly impactful.

Indirect acute effects of the COVID-19 pandemic on physical and mental health in the UK: a population-based study

“…Although restrictions were announced on March 23, 25 public activity levels (measured by mobile phone applications and public transport journeys) had declined before the announcement. 26 , 27 , 28 To account for anticipatory behaviour, we conservatively defined the start of restrictions as March 8, 2020 and removed data for 3 weeks in March up to and including the week restrictions were announced (March 8–28, 2020, inclusive) from this analysis.…”

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

“…The third, the country of study and the references. Category Proxy data type Country and reference Adoption of NPI Mobile phones and/or vehicles through location intelligence and measurement platforms USA [367] , [368] , [369] , [370] , [371] , [372] Twitter Twitter population [373] , [374] Wikipedia and Reddit Canada, Italy, USA, UK [375] Google USA [376] ; 130 countries [377] TV USA [378] Mobility changes Mobile phones and/or vehicles through location intelligence and measurement platforms USA [37] , [38] , [379] , [380] Facebook Italy [39] Citymapper app 22 countries [381] …”

“…About half of the papers that measured NPIs via proxy data focused on the changes in mobility that travel bans, school closure, remote working, local and national lockdowns have induced in the population. While there is rich variety of data sources used, this data is collected via mobile phones by location intelligence and measurement platforms [37] , [38] , [368] , [369] , [379] , [380] , tech giants such as Facebook [39] , [367] and Google [377] , [382] , as well as other mobile apps [381] . Indeed the high penetration of smart phones, their portability, the vital role they took in our daily lives, the popularity of some apps and services make them the perfect tool to infer high-resolution data about our movements and their possible changes.…”

“…Authors in Ref. [381] use data from cities in countries collected via the transportation app Citymapper. They study the association between mobility and NPIs policies.…”

Comparison of Deaths Rates for COVID-19 across Europe During the First Wave of the COVID-19 Pandemic

“…These included restrictions on movement outside the home, rules on physical distancing, mandatory face covering in closed public settings, and introduction of elements of find, test, trace, isolate, and support systems. Even where restrictions were minimal, as in Sweden, or delayed, as in the United Kingdom, many people changed their behaviour in ways that reduced risks (2). Unlike the situation in Africa and the Americas, the initial peak of infection in Europe is now subsiding, and while some countries are seeing a resurgence associated with loosening of restrictions, it is timely to take stock of how Europe has fared in terms of deaths.…”