The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology

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.

[1]  C. Whittaker,et al.  Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand , 2020 .

[2]  Munik Shrestha,et al.  Crowding and the epidemic intensity of COVID-19 transmission , 2020, medRxiv.

[3]  Andrew J Tatem,et al.  Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning , 2014, Malaria Journal.

[4]  A. Azman,et al.  Prolonging herd immunity to cholera via vaccination: Accounting for human mobility and waning vaccine effects , 2018, PLoS neglected tropical diseases.

[5]  Chen Shen,et al.  ข้อสรุปจากบทความ “ผลของมาตรการที่ไม่ใช่ยาในการลดอัตราการตายและความต้องการทรัพยาการทาง สาธารณสุขเนื่องจากโรคโควิด-19” โดย Neil Ferguson และคณะ Review of Ferguson et al ”Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand.” , 2020 .

[6]  P. Bajardi,et al.  COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown , 2020, Scientific Data.

[7]  F. Dominici,et al.  Aggregated mobility data could help fight COVID-19 , 2020, Science.

[8]  Bill Gates,et al.  Responding to Covid-19 - A Once-in-a-Century Pandemic? , 2020, The New England journal of medicine.

[9]  Yuhao Kang,et al.  Mapping county-level mobility pattern changes in the United States in response to COVID-19 , 2020, ArXiv.

[10]  Lucie Abeler-Dörner,et al.  Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing , 2020, Science.

[11]  Sangchul Park,et al.  Information Technology-Based Tracing Strategy in Response to COVID-19 in South Korea-Privacy Controversies. , 2020, JAMA.

[12]  A. Tatem,et al.  Commentary: Containing the Ebola Outbreak - the Potential and Challenge of Mobile Network Data , 2014, PLoS currents.

[13]  R. Irizarry,et al.  Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand , 2004, Nature.

[14]  O. Bjørnstad,et al.  Dynamics of measles epidemics: Estimating scaling of transmission rates using a time series sir model , 2002 .

[15]  Alex Rutherford,et al.  On the privacy-conscientious use of mobile phone data , 2018, Scientific Data.

[16]  Andrew J. Tatem,et al.  Identifying Malaria Transmission Foci for Elimination Using Human Mobility Data , 2016, PLoS Comput. Biol..

[17]  Caroline O. Buckee,et al.  The impact of biases in mobile phone ownership on estimates of human mobility , 2013, Journal of The Royal Society Interface.

[18]  David L. Smith,et al.  Quantifying the Impact of Human Mobility on Malaria , 2012, Science.

[19]  Caroline O. Buckee,et al.  Heterogeneous Mobile Phone Ownership and Usage Patterns in Kenya , 2012, PloS one.

[20]  D. Patrick,et al.  High SARS-CoV-2 Attack Rate Following Exposure at a Choir Practice - Skagit County, Washington, March 2020. , 2020, MMWR. Morbidity and mortality weekly report.

[21]  T. Hollingsworth,et al.  How will country-based mitigation measures influence the course of the COVID-19 epidemic? , 2020, The Lancet.

[22]  N. Lo,et al.  Scientific and ethical basis for social-distancing interventions against COVID-19 , 2020, The Lancet Infectious Diseases.

[23]  Bryan T Grenfell,et al.  A stochastic model for extinction and recurrence of epidemics: estimation and inference for measles outbreaks. , 2002, Biostatistics.

[24]  G. Gao,et al.  A Novel Coronavirus from Patients with Pneumonia in China, 2019 , 2020, The New England journal of medicine.

[25]  O. Bjørnstad,et al.  Travelling waves and spatial hierarchies in measles epidemics , 2001, Nature.

[26]  A. Tatem,et al.  Effect of non-pharmaceutical interventions to contain COVID-19 in China , 2020, Nature.

[27]  Xin Lu,et al.  Population mobility reductions associated with travel restrictions during the Ebola epidemic in Sierra Leone: use of mobile phone data , 2018, International journal of epidemiology.

[28]  R. F. Grais,et al.  Explaining Seasonal Fluctuations of Measles in Niger Using Nighttime Lights Imagery , 2011, Science.

[29]  Joel Hellewell,et al.  Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS-CoV-2 in different settings: a mathematical modelling study , 2020, The Lancet Infectious Diseases.

[30]  S. Bhatt,et al.  Evidence of initial success for China exiting COVID-19 social distancing policy after achieving containment. , 2020, Wellcome open research.

[31]  Caroline O. Buckee,et al.  Mobile phone data for public health: towards data-sharing solutions that protect individual privacy and national security , 2016, ArXiv.

[32]  Pavel Dedera,et al.  Mathematical Modelling of Study , 2011 .

[33]  Y. Xia,et al.  Measles Metapopulation Dynamics: A Gravity Model for Epidemiological Coupling and Dynamics , 2004, The American Naturalist.

[34]  Caroline O Buckee,et al.  Human movement data for malaria control and elimination strategic planning , 2012, Malaria Journal.

[35]  Nuno R. Faria,et al.  The effect of human mobility and control measures on the COVID-19 epidemic in China , 2020, Science.

[36]  Kenth Engø-Monsen,et al.  Impact of human mobility on the emergence of dengue epidemics in Pakistan , 2015, Proceedings of the National Academy of Sciences.

[37]  Caroline O Buckee,et al.  Connecting Mobility to Infectious Diseases: The Promise and Limits of Mobile Phone Data. , 2016, The Journal of infectious diseases.

[38]  Jianping Huang,et al.  Indirect Virus Transmission in Cluster of COVID-19 Cases, Wenzhou, China, 2020 , 2020, Emerging infectious diseases.

[39]  Jessica T Davis,et al.  The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak , 2020, Science.

[40]  Marco De Nadai,et al.  Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle , 2020, Science Advances.

[41]  Susan Woods,et al.  Community Transmission of SARS-CoV-2 at Two Family Gatherings — Chicago, Illinois, February–March 2020 , 2020, MMWR. Morbidity and mortality weekly report.

[42]  Ramesh Raskar,et al.  Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic , 2020, ArXiv.

[43]  Alex 'Sandy' Pentland,et al.  Assessing Disease Exposure Risk with Location Data: A Proposal for Cryptographic Preservation of Privacy , 2020, 2003.14412.

[44]  A. Tatem,et al.  Measles outbreak risk in Pakistan: exploring the potential of combining vaccination coverage and incidence data with novel data-streams to strengthen control , 2018, Epidemiology and Infection.

[45]  Kai Zhao,et al.  A pneumonia outbreak associated with a new coronavirus of probable bat origin , 2020, Nature.

[46]  L. Bengtsson,et al.  Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti , 2011, PLoS medicine.

[47]  A. Wilder-Smith,et al.  The global community needs to swiftly ramp up the response to contain COVID-19 , 2020, The Lancet.

[48]  Song Gao,et al.  Mapping county-level mobility pattern changes in the United States in response to COVID-19 , 2020, ACM SIGSPATIAL Special.