COVID-19 lockdown induces disease-mitigating structural changes in mobility networks

Frank Schlosser; Benjamin F. Maier; Olivia Jack; David Hinrichs; Adrian Zachariae; Dirk Brockmann

doi:10.1073/pnas.2012326117

Edited by Nils Chr. Stenseth, University of Oslo, Oslo, Norway, and approved October 20, 2020 (received for review June 26, 2020)

Significance

During the COVID-19 pandemic, mobility restrictions have proved to be an effective mitigation strategy in many countries. To apply these measures more efficiently in the future, it is important to understand their effects in detail. In this study, we use mobile phone data to uncover profound structural changes in mobility in Germany during the pandemic. We find that a strong reduction of long-distance travel rendered mobility to be more local, such that distant parts of the country became less connected. We demonstrate that due to this loss of connectivity, infectious diseases can be slowed down in their spatial spread. Our study provides important insights into the complex effects of mobility restrictions for policymakers and future research.

Abstract

In the wake of the COVID-19 pandemic many countries implemented containment measures to reduce disease transmission. Studies using digital data sources show that the mobility of individuals was effectively reduced in multiple countries. However, it remains unclear whether these reductions caused deeper structural changes in mobility networks and how such changes may affect dynamic processes on the network. Here we use movement data of mobile phone users to show that mobility in Germany has not only been reduced considerably: Lockdown measures caused substantial and long-lasting structural changes in the mobility network. We find that long-distance travel was reduced disproportionately strongly. The trimming of long-range network connectivity leads to a more local, clustered network and a moderation of the “small-world” effect. We demonstrate that these structural changes have a considerable effect on epidemic spreading processes by “flattening” the epidemic curve and delaying the spread to geographically distant regions.

Footnotes

↵¹To whom correspondence may be addressed. Email: frank.schlosser{at}hu-berlin.de.

Author contributions: F.S., B.F.M., and D.B. designed research; F.S. and B.F.M. performed research; F.S., B.F.M., O.J., D.H., and A.Z. analyzed data; and F.S., B.F.M., and D.B. wrote the paper.
The authors declare no competing interest.
This article is a PNAS Direct Submission.
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This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2012326117/-/DCSupplemental.

Data Availability.

The mobile phone dataset is deposited in the Open Science Framework (OSF) repository for the COVID-19 mobility project (38) in an anonymized form, which will enable readers to replicate the main results of this paper (see SI Appendix for a description of the anonymization process). All other datasets used are publicly available: the ACAPS dataset on government policies (6), population data (39), and county-level geodata for Germany (40). Their sources and details are also listed in SI Appendix. The Python code used for the SIR simulation is available at https://github.com/franksh/EpiCommute and included in the OSF repository (38).

This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

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