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

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Science Advances  05 Jun 2020:
Vol. 6, no. 23, eabc0764
DOI: 10.1126/sciadv.abc0764

Figures

  • Fig. 1 Pandemic intervals as defined by the U.S. Centers for Disease Control and the World Health Organization [based on (52)].
  • 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.

Tables

  • Table 1 Examples of questions by areas of inquiry.

    Situational awarenessCause and effect
    • What are the most common
    mobility flows within and
    between COVID-19–affected
    cities and regions?
    • What are variables that
    determine the success of social
    distancing approaches?
    • Which areas are spreading the
    epidemics acting as origin nodes
    in a mobility network and thus
    could be placed under mobility
    restrictions?
    • How do local mobility patterns
    affect the burden on the
    medical system?
    • Are people continuing to travel
    or congregate after social
    distancing and travel restrictions
    were put into place?
    • Are business’ social distancing
    recommendations resulting in
    more workers working from
    home?
    • Are there hotspots at higher risk
    of contamination (due to a
    higher level of mobility and
    higher concentration of
    population)?
    • In what sectors are people
    working most from home?
    • What are the key entry points,
    locations, and movements of
    roamers or tourists?
    • What are the social and
    economic consequences of
    movement restriction
    measures?
    Predictive analysisImpact
    • How are certain human mobility
    patterns likely to affect the
    spread of the coronavirus? And
    what is the likely spread of
    COVID-19, based on existing
    disease models and up-to-date
    mobility data?
    • How have travel restrictions
    affected human mobility
    behavior and likely disease
    transmission?
    • What are the likely effects of
    mobility restrictions on
    children’s education outcomes?
    • What is the potential of various
    restriction measures to avert
    infection cases and save lives?
    • What are likely to be the
    economic consequences of
    restricted mobility for
    businesses?
    • What is the effect of mandatory
    social distancing measures,
    including closure of schools?
    • How has the dissemination of
    public safety information and
    voluntary guidance affected
    human mobility behavior and
    disease spread?

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