This article is a Preprint
Preprints are preliminary research reports that have not been certified by peer review. They should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Preprints posted online allow authors to receive rapid feedback and the entire scientific community can appraise the work for themselves and respond appropriately. Those comments are posted alongside the preprints for anyone to read them and serve as a post publication assessment.
A cell phone data driven time use analysis of the COVID-19 epidemic
Preprint
in English
| medRxiv
| ID: ppmedrxiv-20073098
ABSTRACT
Transmission of the SAR-COV-2 virus that causes COVID-19 is largely driven by human behavior and person-to-person contact. By staying home, people reduce the probability of contacting an infectious individual, becoming infected, and passing on the virus. One of the most promising sources of data on time use is smartphone location data. We develop a time use driven proportional mixing SEIR model that naturally incorporates time spent at home measured using smartphone location data and allows people of different health statuses to behave differently. We simulate epidemics in almost every county in the United States. The model suggests that Americans behavioral shifts have reduced cases in 55%-86% of counties and for 71%-91% of the population, depending on modeling assumptions. Resuming pre-epidemic behavior would lead to a rapid rise in cases in most counties. Spatial patterns of bending and flattening the curve are robust to modeling assumptions. Depending on epidemic history, county demographics, and behavior within a county, returning those with acquired immunity (assuming it exists) to regular schedules generally helps reduce cumulative COVID-19 cases. The model robustly identifies which counties would experience the greatest share of case reduction relative to continued distancing behavior. The model occasionally mischaracterizes epidemic patterns in counties tightly connected to larger counties that are experiencing large epidemics. Understanding these patterns is critical for prioritizing testing resources and back-to-work planning for the United States.
cc_by_nc_nd
Full text:
Available
Collection:
Preprints
Database:
medRxiv
Type of study:
Prognostic study
Language:
English
Year:
2020
Document type:
Preprint