Cell-phone traces reveal infection-associated behavioral change.
Proc Natl Acad Sci U S A
; 118(6)2021 02 09.
Article
in English
| MEDLINE | ID: covidwho-1371647
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; [Formula see text]), on average, in the 2 to 4 d around diagnosis and place fewer calls (2.3 to 3.3 fewer calls; [Formula see text]) while spending longer on the phone (41- to 66-s average increase; [Formula see text]) 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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Behavior
/
Communicable Diseases
/
Cell Phone
Type of study:
Diagnostic study
/
Observational study
/
Prognostic study
Topics:
Long Covid
Limits:
Humans
Country/Region as subject:
Europa
Language:
English
Year:
2021
Document Type:
Article
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