Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle

By Nuria Oliver, Bruno Lepri, Harald Sterly, Renaud Lambiotte, Sébastien Delataille, Marco De Nadai, Emmanuel Letouzé, A. A. Salah, Richard Benjamins, Ciro Cattuto and 14 more. Science Advances, 2020

620 citations recorded by the source corpus.

The coronavirus 2019–2020 pandemic (COVID-19) poses unprecedented challenges for governments and societies around the world ( 1 ). Nonpharmaceutical interventions have proven to be critical for delaying and containing the COVID-19 pandemic ( 2 – 6 ). These include testing and tracing, bans on large gatherings, nonessential business and school and university closures, international and domestic mobility restrictions and physical isolation, and total lockdowns of regions and countries. Decision-making and evaluation or such interventions during all stages of the pandemic life cycle require specific, reliable, and timely data not only about infections but also about human behavior, especially mobility and physical copresence. We argue that mobile phone data, when used properly and carefully, represents a critical arsenal of tools for supporting public health actions across early-, middle-, and late-stage phases of the COVID-19 pandemic. Seminal work on human mobility has shown that aggregate and (pseudo-)anonymized mobile phone data can assist the modeling of the geographical spread of epidemics ( 7 – 11 ). Thus, researchers and governments have started to collaborate with private companies, most notably mobile network operators and location intelligence companies, to estimate the effectiveness of control measures in a number of countries, including Austria, Belgium, Chile, China, Germany, France, Italy, Spain, United Kingdom, and the United States ( 12 – 21 ). There is, however, little coordination or information exchange between these national or even regional initiatives ( 22 ). Although ad hoc mechanisms leveraging mobile phone data can be effectively (but not easily) developed at the local or national level, regional or even global collaborations seem to be much more difficult given the number of actors, the range of interests and priorities, the variety of legislations concerned, and the need to protect civil liberties. The global scale and spread of the COVID-19 pandemic highlight the need for a more harmonized or coordinated approach. In the …

Subfields: Information Systems, Modeling and Simulation, Transportation

View Paper: https://www.semanticscholar.org/paper/5bd2c582b8f2dd71653dc0ce2534d85dbb0cc629

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Citations in the corpus

The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology (2020, background): “These applications, metrics developed to analyze these data, and proposed best practices have recently been reviewed by an interdisciplinary team of experts 28 .”
Leveraging Data Science to Combat COVID-19: A Comprehensive Review (2020, background): “Mobility trace data [65] can also serve a similar purpose—a collection of such logs is available here [66].”
Non-pharmaceutical interventions during the COVID-19 pandemic: A review (2020, background): “[29] Nuria Oliver, Bruno Lepri, Harald Sterly, Renaud Lambiotte, Sébastien Deletaille, Marco De Nadai, Emmanuel Letouzé, Albert Ali Salah, Richard Benjamins, Ciro Cattuto, Vittoria Colizza, Nicolas de Cordes, Samuel P.”
Mapping county-level mobility pattern changes in the United States in response to COVID-19 (2020, background): “It demonstrated the value of tracking human mobility patterns in the infectious disease research and in public health policy making [5, 17].”
A Survey on Deep Learning for Human Mobility (2020, background): “, the COVID-19 pandemic) [101, 105, 127, 130, 141, 156], the behavior of people in case of natural disasters [83, 175, 199], the public and private transportation and the resulting traffic volumes [31, 58, 94, 153], the well-being of citizens [137, 176, 192], the severity of air pollution, energy an”
Mobile phone location data for disasters: A review from natural hazards and epidemics (2021, neutral): “(Oliver et al., 2020) The attention and interest towards mobile phone location data from government agencies, researchers, and the public, has never been higher.”
A Deep Gravity model for mobility flows generation (2020, background): “…mobility patterns in urban environments 15,20 – 24 , the estimation of city population 25 – 27 , the migration induced by natural disasters, climate change, and con ﬂ icts 28 – 32 , the prediction of traf ﬁ c and crowd ﬂ ows 14,33 – 37 , and the forecasting of the spreading of epidemics 38 – 42 .”
COVID-19 lockdown induces disease-mitigating structural changes in mobility networks (2020, positive): “Based on various digital data sources such as mobile phone data or social media data, several studies show that mobility significantly changed during lockdowns (7).”