Aggregated mobility data could help fight COVID-19
As the coronavirus disease 2019 (COVID-19) epidemic worsens, understanding the effectiveness of public messaging and large-scale social distancing interventions is critical. The research and public health response communities can and should use population mobility data collected by private companies, with appropriate legal, organizational, and computational safeguards in place. When aggregated, these data can help refine interventions by providing near real-time information about changes in patterns of human movement.
Research groups and nonprofit humanitarian agencies have refined data use agreements to stipulate clear guidelines that ensure responsible data practices (1). New tools for specifying different levels of privacy for different users and providing privacy-preserving results, such as the OpenDP platform (2), will effectively manage data access, and aggregation steps have been carefully reviewed on a legal and methodological basis to ensure that the analyses follow ethical guidelines for human participants (3). To monitor social distancing interventions, for example, rather than showing individual travel or behavior patterns, information from multiple devices is aggregated in space and time, so that the data reflect an approximation of population-level mobility (4).
The estimates of aggregate flows of people are incredibly valuable. A map that examines the impact of social distancing messaging or policies on population mobility patterns, for example, will help county officials understand what kinds of messaging or policies are most effective. Comparing the public response to interventions, in terms of the rate of movement over an entire county from one day to the next, measured against a baseline from normal times, can provide insight into the degree to which recommendations on social distancing are being followed. We will need these estimates, not only now but also when we need to resume life again without risking a major resurgence.
The protection of personal privacy must be paramount. Consent-based data sharing models and data protection laws provide for the legal grounds to use personal data during emergencies, but we do not advocate the use of individual data (5, 6). The measures proposed do not need to run afoul of data protection goals, as a recent statement by the Chair of the European Data Protection Board in the context of the COVID-19 outbreak clarifies (7).
There are already precedents in Asia and Europe (8). Deutsche Telekom has shared aggregated data with Germany to help measure social distancing, in compliance with EU laws (9). The more such analyses are initiated and concluded openly, and in accordance with the law, the greater will be the public trust and our ability to produce reliable analytic insights. Associated risks should be thoughtfully addressed and weighed against the benefits of the data, which could help reduce the death toll in vulnerable populations.
References and Notes
1
F. Greenwood et al., “The signal code: A human rights approach to information during crisis” (Signal Program on Human Security and Technology, Harvard Humanitarian Initiative, 2015); https://hhi.harvard.edu/sites/default/files/publications/signalcode_final.pdf.
2
Harvard University Privacy Tools and Privacy Insights Project, OpenDP (http://opendp.io).
3
Y. de Montjoye et al., Sci. Data 5, 180286 (2018).
4
P. Maas et al., “Facebook disaster maps: Aggregate insights for crisis response & recovery,” Facebook (2019); https://research.fb.com/wp-content/uploads/2019/04/iscram19_camera_ready.pdf.
5
California Consumer Privacy Act (2020); https://oag.ca.gov/privacy/ccpa.
6
B. Puckett, S. J. McMenemy, “Maintaining employees' privacy during a public health crisis,” National Law Review (2020); www.natlawreview.com/article/maintaining-employees-privacy-during-public-health-crisis.
7
“Statement of the EDPB Chair on the processing of personal data in the context of the COVID-19 outbreak” (2020); https://edpb.europa.eu/sites/edpb/files/files/news/edpb_covid-19_20200316_press_statement_en.pdf.
8
S. Lai et al., “Assessing spread risk of Wuhan novel coronavirus within and beyond China,” medRxiv 2020.02.04.20020479 (2020); https://doi.org/10.1101/2020.02.04.20020479.
9
E. Pollina, D. Busvine, “European mobile operators share data for coronavirus fight,” Reuters (2020).
Competing Interests
S.V.S. is a member of the Scientific Advisory Board of BioFire Diagnostic's Trend Surveillance System, which includes paid consulting work.
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Volume 368 | Issue 6487
10 April 2020
10 April 2020
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Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
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S.V.S. is a member of the Scientific Advisory Board of BioFire Diagnostic's Trend Surveillance System, which includes paid consulting work.
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RE:
Existing consumer mobile technology can be used in a privacy compliant and anonymous way to identify at risk patients and populations, help public health officials identify disease hotspots, and forecast where future outbreaks may occur.
Current consumer mobile technology and privacy policies can be leveraged in order to create ongoing permissions-based datasets.
Mobile application developer agreements with Google and Apple allow for apps to be developed that use mobile phone application programming interfaces (APIs) to share location data on an opt-in basis. Consumers explicitly permit sharing their latitude and longitude and data is matched to an anonymous mobile application identifier (MAID) from either Apple (IDFA) or Google (GAID). If a user's email or name is not requested, there is no Personally Identifiable Information (PII) or Personal Health Information (PHI) at risk, even if a consumer opts to share their disease status.
We built an application that does this. Users choose to download a mobile application and then explicitly choose to share their location data on an on-going basis. They choose to select their disease status (symptomatic, positive, etc.) and have the option to change that status. Their location data and disease status is added to an anonymous database. Other users can be alerted if they have interacted with a mobile device owned by a self-identified positive patient without being told who that person is and public health officials can identify disease spread in aggregate.
Because the application follows existing privacy rules and regulations, all data is anonymous, no historic information is shared (e.g., data before the application was downloaded) and nothing is shared without the consumer's explicit permission.
Our application is currently being submitted for Apple review and we plan to trial this approach in conjunction with researchers from Johns Hopkins School of Public Health, as soon as next week.