Abstract

The ongoing coronavirus disease 2019 (COVID-19) pandemic has heightened discussion of the use of mobile phone data in outbreak response. Mobile phone data have been proposed to monitor effectiveness of non-pharmaceutical interventions, to assess potential drivers of spatiotemporal spread, and to support contact tracing efforts. While these data may be an important part of COVID-19 response, their use must be considered alongside a careful understanding of the behaviors and populations they capture. Here, we review the different applications for mobile phone data in guiding and evaluating COVID-19 response, the relevance of these applications for infectious disease transmission and control, and potential sources and implications of selection bias in mobile phone data. We also discuss best practices and potential pitfalls for directly integrating the collection, analysis, and interpretation of these data into public health decision making.

In this Perspective, the authors review the different applications for mobile phone data to support COVID-19 pandemic response, the relevance of these applications for infectious disease transmission and control, and potential sources and implications of selection bias in mobile phone data.

Details

Title
The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology
Author
Grantz, Kyra H 1 ; Meredith, Hannah R 1   VIAFID ORCID Logo  ; Cummings Derek A T 2   VIAFID ORCID Logo  ; Metcalf C Jessica E 3   VIAFID ORCID Logo  ; Grenfell, Bryan T 3 ; Giles, John R 1   VIAFID ORCID Logo  ; Mehta Shruti 1 ; Solomon, Sunil 1 ; Labrique Alain 4 ; Kishore Nishant 5   VIAFID ORCID Logo  ; Buckee, Caroline O 5   VIAFID ORCID Logo  ; Wesolowski, Amy 1   VIAFID ORCID Logo 

 Johns Hopkins Bloomberg School of Public Health, Department of Epidemiology, Baltimore, USA (GRID:grid.21107.35) (ISNI:0000 0001 2171 9311) 
 University of Florida, Department of Biology and the Emerging Pathogens Institute, Gainesville, USA (GRID:grid.15276.37) (ISNI:0000 0004 1936 8091) 
 Princeton University, Department of Ecology and Evolutionary Biology and the Woodrow Wilson School of International and Public Affairs, Princeton, USA (GRID:grid.16750.35) (ISNI:0000 0001 2097 5006) 
 Johns Hopkins Bloomberg School of Public Health, Department of International Health, Baltimore, USA (GRID:grid.21107.35) (ISNI:0000 0001 2171 9311) 
 Harvard TH Chan School of Public Health, Department of Epidemiology and the Center for Communicable Disease Dynamics, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X) 
Publication year
2020
Publication date
2020
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2449448773
Copyright
© The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.