Evidence map›Paper›PMID 32967691›Full record

ArticleGlobalization and health2020

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.

Matia Vannoni, Martin McKee, Jan C Semenza, Chris Bonell, David Stuckler

Open access · goldAbstract read
In one paragraph

Article in Globalization and health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
14.3field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 61 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Human mobility and the infectious disease transmission: A systematic review.Geo-spatial information science = Diqui kongjian xinxi kexue xuebao · 2024
    Article
  7. Article
  8. Impact of COVID-19 Pandemic on Daily Mobility of the Elderly Living in Small Cities in Lodz Province.International journal of environmental research and public health · 2023
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Autoregressive count data modeling on mobility patterns to predict cases of COVID-19 infection.Stochastic environmental research and risk assessment : research journal · 2022
    Article
  17. Review
  18. Research into policy: lessons from the COVID-19 pandemic.European journal of public health · 2021
    Article
  19. Review
  20. Global impact of COVID-19 pandemic on road traffic collisions.World journal of emergency surgery : WJES · 2021
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 4 institutions in 3 countries.

Matia VannoniDepartment of Political Economy, King's College London, Bush House North East Wing, 30 Aldwych, London, WC2B 4BG, UK.ORCID 0000-0002-8335-6402
Martin McKeeFaculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK.ORCID 0000-0002-0121-9683
Jan C SemenzaDondena Centre for Research on Social Dynamics and Public Policy and Department of Social & Political Sciences, Bocconi University, Milan, Italy.ORCID 0000-0002-4625-874X
Chris BonellEuropean Centre for Disease Prevention and Control, Gustav III:s Boulevard 40, 169 73, Solna, Sweden.ORCID 0000-0002-6253-6498
David StucklerDondena Centre for Research on Social Dynamics and Public Policy and Department of Social & Political Sciences, Bocconi University, Milan, Italy. david.stuckler@unibocconi.it.ORCID 0000-0002-1288-8401
Bocconi University · ITEuropean Centre for Disease Prevention and Control · SEKing's College London · GBLondon School of Hygiene & Tropical Medicine · GB

Funding

Wellcome TrustWellcome Trust N/a
6 · The paper itself

Abstract

objectivesRestricting 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.

designTrends 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.

setting41 cities worldwide.

main outcome measuresCitymapper's mobility index.

resultsMobility 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.

conclusionsUnderstanding 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.

Indexed as

Global HealthCitiesCoronavirus InfectionsCOVID-19Geographic Information SystemsHumansPandemicsPneumonia, ViralPublic PolicyTime FactorsTravelVolunteers

Identifiers

PMID32967691
PMCPMC7509494
OpenAlexW3087716147

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.