We are a network of infectious disease epidemiologists at universities around the world working with technology companies to use aggregated mobility data to support the COVID-19 response. Our goal is to provide daily updates to decision-makers at the state and local levels on how well social distancing interventions are working, using anonymized, aggregated data sets from mobile devices, along with analytic support for interpretation.
This site provides a platform for sharing of data that can safely be made public.
The Network provides openly shared resources for researchers interested in using these data to conduct work relevant for controlling the COVID-19 outbreak and the health impacts on populations around the world.
Network members share code repositories, analytic tools and best practices via GitHub repositories and online community meeting spaces.
This table includes a description of commonly used metrics to measure mobility, and will be useful to policy makers, media, and communities.
We maintain a document that outlines
This document provides information about how different sources of data compare to each other. In some cases, the metrics correlate well, but not all. We will be systematically making such comparisons across data as we progress, and providing information about comparability and uncertainty on our site.
As more data become available, Network researchers expect to assist the development of guidelines on how these metrics can inform the relaxation of restrictions, serological surveillance and community-based testing.
Network researchers provide daily one-to-one debriefs to local departments of health and response agencies, so scientists may directly assist in interpreting the data and analysis.
The response to COVID-19 outbreak has demonstrated the extent of human mobility data available to technology companies, data aggregators and governments around the world. It is critical to proceed with caution - the misinterpretation of such data can result in misdirected policies; the misuse of such data can result in harm to individuals and groups. Network members adhere to the data use policy published on our website; only work with aggregated anonymized data analyzed at a temporal or spatial resolution that minimizes harm; and do not promote the use of data at a resolution that cannot be scientifically justified for the interventions proposed.
In response to the COVID-19 outbreak, technology companies and mobile phone operators are coming forward to provide important, epidemiologically-relevant data from mobile devices, that could help to inform policies that reduce the spread of the virus. For example, aggregated location data from mobile phones can be used to provide important population-level information about how people are moving around. Given that social distancing interventions — including travel restrictions, working from home, closing schools, universities, and non-essential retail and restaurant venues, and canceling social gatherings — are important interventions available to slow transmission in the early stage of the epidemic, monitoring how well public messaging is working will be key to its successful implementation. Measuring the impact of social distancing interventions on movement patterns, and subsequently on the spread of COVID-19, will therefore be an essential prerequisite to generate evidence for when we relax restrictions to resume our lives and livelihoods while managing the risk of resurgence.
Our position on the use of these data for combatting the Covid-19 pandemic is articulated in this letter published in Science, on March 23rd, 2020.
It is important to recognize that the aggregated mobility metrics now available through a surge of publicly available dashboards are just one source of information about behavioral responses to interventions, and to consider their limitations. Different data sources will produce mobility estimates that reflect the demographic and geographic coverage of the owners of devices they come from. We expect these to be biased, both across different geographies and among different demographic groups, and must therefore be interpreted in their particular social, economic and political context. The inability of communities to physically distance, for example, is most often a reflection of underlying economic realities that preclude working from home or not working at all, and warrants supportive measures that allow people to physically distance, to the extent feasible. For denser populations in the global south, physical distancing may not be an option.
Such analysis would greatly benefit from the several years of epidemiological research conducted using mobility data. The Network makes available trained scientists to serve as trusted intermediaries between the aggregated data received from technology companies and the policy makers and communities that seek to use them.
The participants in the COVID19 Mobility Data Network share a deep commitment to privacy values and data protection as well as best practice principles related to data governance and ethics. The following principles guide the Network’s effort:
The Network itself will not be the recipient of data. The use of data facilitated by the Network will be subject to the various approval and oversight mechanisms provided by the academic home institutions of the individual researchers participating in the Network. The Network facilitates the sharing of consolidated daily situation reports with government health officials and provides analytic support. It does not share the underlying data sets with governments, third parties, or the public at large. Participants in the Network embrace appropriate legal, organizational, and computational safeguards to minimize - and carefully manage any remaining - data privacy risks associated with this research effort in general and the use of aggregated data in particular.
Micah Altman, Alexandra Wood, David O’Brien, Salil Vadhan, and Urs Gasser. 2015. Towards a Modern Approach to Privacy-Aware Government Data Releases. 30 Berkeley Tech. L. J. 1967, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2779266
Ben Green, Gabe Cunningham, Ariel Ekblaw, Paul Kominers, Andrew Linzer, and Susan Crawford. 2017. Open Data Privacy (2017). Berkman Klein Center for Internet & Society Research Publication, Link at Harvard.edu.
Simson L. Garfinkel. 2016. De-identifying Government Datasets. NIST Special Publication 800-188 (2nd Draft). https://csrc.nist.gov/csrc/media/publications/sp/800-188/draft/documents/sp800_188_draft2.pdf
National Academies of Sciences. 2017. Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy. https://www.nap.edu/catalog/24652/innovations-in-federal-statistics-combining-data-sources-while-protecting-privacy
National Academies of Sciences. 2017. Federal Statistics, Multiple Data Sources, and Privacy Protection: Next Steps. https://www.nap.edu/catalog/24893/federal-statistics-multiple-data-sources-and-privacy-protection-next-steps
Marcel Salathé, Linus Bengtsson, Todd J. Bodnar, Devon D. Brewer, John S. Brownstein, Caroline Buckee, Ellsworth M. Campbell, Ciro Cattuto, Shashank Khandelwal, Patricia L. Mabry, Alessandro Vespignani. 2012. Digital Epidemiology. PLOS Computational Biology. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002616
Yves-Alexandre de Montjoye, Sébastien Gambs, […], and Linus Bengtsson. 2018. On the privacy-conscientious use of mobile phone data. Scientific Data. https://www.ncbi.nlm.nih.gov/pubmed/30532052
You may contact the team with the email address below.
We are not able to respond to every inquiry, however, if you are an infectious disease epidemiologist or modeler, who has a relationship with local/state public health and would be interested in being a part of our Network, please contact us.
Please note all researchers are required to review and sign our Network Data Use Policy, as well as signing Data Use Agreements with technology companies.
Media inquiries may also be submitted via email.