Domestic and international mobility trends in the United Kingdom during the COVID-19 pandemic: an analysis of facebook data

  1. Harry E. R. Shepherd
  2. Florence S. Atherden
  3. Ho Man Theophilus Chan
  4. Alexandra Loveridge
  5. Andrew J. Tatem

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Abstract

Background

Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which resulted in changes to mobility across different regions. An understanding of how these policies impacted travel patterns over time and at different spatial scales is important for designing effective strategies, future pandemic planning and in providing broader insights on the population geography of the country. Crowd level data on mobile phone usage can be used as a proxy for population mobility patterns and provide a way of quantifying in near-real time the impact of social distancing measures on changes in mobility.

Methods

Here we explore patterns of change in densities, domestic and international flows and co-location of Facebook users in the UK from March 2020 to March 2021.

Results

We find substantial heterogeneities across time and region, with large changes observed compared to pre-pademic patterns. The impacts of periods of lockdown on distances travelled and flow volumes are evident, with each showing variations, but some significant reductions in co-location rates. Clear differences in multiple metrics of mobility are seen in central London compared to the rest of the UK, with each of Scotland, Wales and Northern Ireland showing significant deviations from England at times. Moreover, the impacts of rapid changes in rules on international travel to and from the UK are seen in substantial fluctuations in traveller volumes by destination.

Conclusions

While questions remain about the representativeness of the Facebook data, previous studies have shown strong correspondence with census-based data and alternative mobility measures, suggesting that findings here are valuable for guiding strategies.

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  1. Version published to 10.1186/s12942-021-00299-5
  2. ScreenIT

    SciScore for 10.1101/2021.08.03.21261545: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The data are not publicly available but are available to researchers by application to the Facebook Data for Good Partner Program (https://dataforgood.fb.com/, diseaseprevmaps(at)fb.com).
    Good Partner Program
    suggested: None
    This was calculated separately for travel in and out of the UK (Highest mean weekly movement out: UK - Ireland, week commencing 10th August 2020; Highest mean weekly movement in: Spain - UK, week commencing 10th March 2020).
    August
    suggested: None

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2021.08.03.21261545v2 on medRxiv
  4. Version published to 10.1101/2021.08.03.21261545v1 on medRxiv