Cell-phone traces reveal infection-associated behavioral change

• Type of Material: Article
• Authors: Ymir Vigfusson, Emory University; Thorgeir Karlsson, Reykjavik University; Derek Onken, Emory University; Congzheng Song, Cornell University; Atli F. Einarsson, Reykjavik University; Nishant Kishore, Harvard University; Rebecca Mitchell, Emory University; Ellen Brooks-Pollock, University of Bristol; Gudrun Sigmundsdottir, Landspitali University Hospital; Leon Danon, University of Bristol
• Language: English
• Date: 2021-02-09
• Publisher: National Academy of Sciences
• Publication Version: Final Published Version
• Copyright Statement: © 2021 the Author(s). Published by PNAS.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1073/pnas.2005241118
• Title of Journal or Parent Work: Proceedings of the National Academy of Sciences of the United States of America
• Volume: 118
• Issue: 6
• Grant/Funding Information: 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.; A hardware donation from NVIDIA Corporation.; 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
• Keywords: Poverty; surveillance; Impact; COVID-19; outbreak; Influenza; disease; call detail records; influenza; Multidisciplinary Sciences; Human mobility; Science & Technology; Science & Technology - Other Topics
• Research Categories: Mathematics; Health Sciences, Public Health; Computer Science; Health Sciences, Epidemiology

Abstract

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.