dc.contributor.author | Oliver, Nuria | |
dc.contributor.author | Lepri, Bruno | |
dc.contributor.author | Sterly, Harald | |
dc.contributor.author | Lambiotte, Renaud | |
dc.contributor.author | Delataille, Sébastien | |
dc.contributor.author | De Nadai, Marco | |
dc.contributor.author | Letouzé, Emmanuel | |
dc.contributor.author | Salah, Albert Ali | |
dc.contributor.author | Benjamins, Richard | |
dc.contributor.author | Cattuto, Ciro | |
dc.contributor.author | Colizza, Vittoria | |
dc.contributor.author | de Cordes, Nicolas | |
dc.contributor.author | Fraiberger, Samuel P. | |
dc.contributor.author | Koebe, Till | |
dc.contributor.author | Lehmann, Sune | |
dc.contributor.author | Murillo, Juan | |
dc.contributor.author | Pentland, Alex | |
dc.contributor.author | Pham, Phuong N | |
dc.contributor.author | Pivetta, Frédéric | |
dc.contributor.author | Saramäki, Jari | |
dc.contributor.author | Scarpino, Samuel V. | |
dc.contributor.author | Tizzoni, Michele | |
dc.contributor.author | Verhulst, Stefaan | |
dc.contributor.author | Vinck, Patrick | |
dc.date.accessioned | 2020-05-11T16:39:16Z | |
dc.date.available | 2020-05-11T16:39:16Z | |
dc.date.issued | 2020-04 | |
dc.identifier.issn | 2375-2548 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/125147 | |
dc.description.abstract | The coronavirus 2019-2020 pandemic (COVID-19) poses unprecedented challenges for governments and societies around the world (1). Non-pharmaceutical interventions (NPIs) have proven to be critical for delaying and containing the COVID-19 pandemic (2–6). This includes testing and tracing, bans on large gatherings, non-essential 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 lifecycle require specific, reliable and timely data not only about infections, but also about human behavior, especially mobility and physical co-presence. 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. | en_US |
dc.publisher | American Association for the Advancement of Science (AAAS) | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1126/sciadv.abc0764 | en_US |
dc.rights | Creative Commons Attribution NonCommercial License 4.0 | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | en_US |
dc.source | Science Advances | en_US |
dc.title | Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Oliver, Nuria et al. "Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle." Science Advances (April 2020): eabc0764 © 2020 The Authors | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Media Laboratory | en_US |
dc.relation.journal | Science Advances | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dspace.date.submission | 2020-05-08T17:28:36Z | |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Complete | |