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Epidemic preparedness depends on our ability to predict the trajectory of an epidemic and the human behavior that drives spread in the event of an outbreak. Changes to behavior during an outbreak limit the reliability of syndromic surveillance using large-scale data sources, such as online social media or search behavior, which could otherwise supplement healthcare-based outbreak-prediction methods. Here, we measure behavior change reflected in mobile-phone call-detail records (CDRs), a source of passively collected real-time behavioral information, using an anonymously linked dataset of cell-phone users and their date of influenza-like illness diagnosis during the 2009 H1N1v pandemic. We demonstrate that mobile-phone use during illness differs measurably from routine behavior: Diagnosed individuals exhibit less movement than normal (1.1 to 1.4 fewer unique tower locations; P < 3.2 × 10−3), on average, in the 2 to 4 d around diagnosis and place fewer calls (2.3 to 3.3 fewer calls; P < 5.6 × 10−4) while spending longer on the phone (41- to 66-s average increase; P < 4.6 × 10−10) than usual on the day following diagnosis. The results suggest that anonymously linked CDRs and health data may be sufficiently granular to augment epidemic surveillance efforts and that infectious disease-modeling efforts lacking explicit behavior-change mechanisms need to be revisited.
|Journal||Proceedings of the National Academy of Sciences of the United States of America|
|Publication status||Published - 9 Feb 2021|
Bibliographical noteFunding Information:
ACKNOWLEDGMENTS. The work was partially supported by Icelandic Centre for Research Award 152620-051; an Emory University Research Council Award; NSF Faculty Early Career Development (CAREER) Grant 1553579; and a hardware donation from NVIDIA Corporation. L.D. was supported by the Leverhulme Trust Early Career Fellowship and The Alan Turing Institute Engineering and Physical Sciences Research Council Grant EP/N510129/1. L.D. and E.B.-P. are supported by Medical Research Council Grants MC PC 19067 and MR/V038613/1. E.B.-P. acknowledges support from the National Institute for Health Research (NIHR) Health Protection Research Unit in Evaluation of Interventions at the University of Bristol.
© 2021 National Academy of Sciences. All rights reserved.
- Call detail records
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- 4 Finished
8032 COVID-19: Spatial heterogeneity in transmission and the impact of interventions: a mathematical modelling approach
18/01/21 → 30/09/21
COVID-19 Modelling Consortium: quantitative epidemiological predictions in response to an evolving pandemic’
19/11/20 → 18/05/22
Project: Research, Parent