Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics

  1. Aniruddha Adiga
  2. Lijing Wang
  3. Adam Sadilek
  4. Ashish Tendulkar
  5. Srinivasan Venkatramanan
  6. Anil Vullikanti
  7. Gaurav Aggarwal
  8. Alok Talekar
  9. Xue Ben
  10. Jiangzhuo Chen
  11. Bryan Lewis
  12. Samarth Swarup
  13. Milind Tambe
  14. Madhav Marathe

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Abstract

This work quantifies mobility changes observed during the different phases of the pandemic world-wide at multiple resolutions – county, state, country – using an anonymized aggregate mobility map that captures population flows between geographic cells of size 5 km 2 . As we overlay the global mobility map with epidemic incidence curves and dates of government interventions, we observe that as case counts rose, mobility fell and has since then seen a slow but steady increase in flows. Further, in order to understand mixing within a region, we propose a new metric to quantify the effect of social distancing on the basis of mobility.Taking two very different countries sampled from the global spectrum, We analyze in detail the mobility patterns of the United States (US) and India. We then carry out a counterfactual analysis of delaying the lockdown and show that a one week delay would have doubled the reported number of cases in the US and India. Finally, we quantify the effect of college students returning back to school for the fall semester on COVID-19 dynamics in the surrounding community. We employ the data from a recent university outbreak (reported on August 16, 2020) to infer possible R eff values and mobility flows combined with daily prevalence data and census data to obtain an estimate of new cases that might arrive on a college campus. We find that maintaining social distancing at existing levels would be effective in mitigating the extra seeding of cases. However, potential behavioral change and increased social interaction amongst students (30% increase in R eff ) along with extra seeding can increase the number of cases by 20% over a period of one month in the encompassing county. To our knowledge, this work is the first to model in near real-time, the interplay of human mobility, epidemic dynamics and public policies across multiple spatial resolutions and at a global scale.

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    SciScore for 10.1101/2020.06.05.20123760: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your data.

    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.

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  2. Version published to 10.1101/2020.06.05.20123760v4 on medRxiv
  3. Version published to 10.1101/2020.06.05.20123760v3 on medRxiv
  4. Version published to 10.1101/2020.06.05.20123760v2 on medRxiv
  5. Version published to 10.1101/2020.06.05.20123760v1 on medRxiv