A spatiotemporal decay model of human mobility when facing large-scale crises.
Proc Natl Acad Sci U S A
; 119(33): e2203042119, 2022 08 16.
Article
in English
| MEDLINE | ID: covidwho-2268839
ABSTRACT
A common feature of large-scale extreme events, such as pandemics, wildfires, and major storms is that, despite their differences in etiology and duration, they significantly change routine human movement patterns. Such changes, which can be major or minor in size and duration and which differ across contexts, affect both the consequences of the events and the ability of governments to mount effective responses. Based on naturally tracked, anonymized mobility behavior from over 90 million people in the United States, we document these mobility differences in space and over time in six large-scale crises, including wildfires, major tropical storms, winter freeze and pandemics. We introduce a model that effectively captures the high-dimensional heterogeneity in human mobility changes following large-scale extreme events. Across five different metrics and regardless of spatial resolution, the changes in human mobility behavior exhibit a consistent hyperbolic decline, a pattern we characterize as "spatiotemporal decay." When applied to the case of COVID-19, our model also uncovers significant disparities in mobility changes-individuals from wealthy areas not only reduce their mobility at higher rates at the start of the pandemic but also maintain the change longer. Residents from lower-income regions show a faster and greater hyperbolic decay, which we suggest may help account for different COVID-19 rates. Our model represents a powerful tool to understand and forecast mobility patterns post emergency, and thus to help produce more effective responses.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Models, Statistical
/
Pandemics
/
Human Migration
/
COVID-19
/
Natural Disasters
Type of study:
Etiology study
/
Observational study
/
Prognostic study
Limits:
Humans
Country/Region as subject:
North America
Language:
English
Journal:
Proc Natl Acad Sci U S A
Year:
2022
Document Type:
Article
Affiliation country:
Pnas.2203042119
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