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Editorial
. 2020 Jun 5;6(23):eabc0764.
doi: 10.1126/sciadv.abc0764. eCollection 2020 Jun.

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

Nuria Oliver  1   2 , Bruno Lepri  2   3 , Harald Sterly  4 , Renaud Lambiotte  5   6 , Sébastien Deletaille  7 , Marco De Nadai  3 , Emmanuel Letouzé  2   8 , Albert Ali Salah  2   9 , Richard Benjamins  10   11 , Ciro Cattuto  12   13 , Vittoria Colizza  14 , Nicolas de Cordes  15 , Samuel P Fraiberger  16 , Till Koebe  2   17 , Sune Lehmann  18 , Juan Murillo  19 , Alex Pentland  20 , Phuong N Pham  2   21 , Frédéric Pivetta  22 , Jari Saramäki  23 , Samuel V Scarpino  24 , Michele Tizzoni  12 , Stefaan Verhulst  25 , Patrick Vinck  2   21
Affiliations
Editorial

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

Nuria Oliver et al. Sci Adv. .
No abstract available

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Figures

Fig. 1
Fig. 1. Pandemic intervals as defined by the U.S. Centers for Disease Control and the World Health Organization [based on (52)].
Fig. 2
Fig. 2. Extraction of aggregated metrics from mobile phone data.
(A) Raw data representing 1-day mobility of two users. In this example, the area B is a hotspot, as it shows a high concentration of people. (B) OD matrix of five different areas, counting the number of trips from one area (rows) to another area (columns). (C) Contact matrix counting the number of potential face-to-face interactions between age groups. (D) Percentage of time spent at home, work, and other locations.
See this image and copyright information in PMC

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