Evidence map›Paper›PMID 37478056›Full record

ArticlePLOS global public health2023

Fine scale human mobility changes within 26 US cities in 2020 in response to the COVID-19 pandemic were associated with distance and income.

Rohan Arambepola, Kathryn L Schaber, Catherine Schluth, Angkana T Huang, Alain B Labrique, Shruti H Mehta, Sunil S Solomon, Derek A T Cummings, Amy Wesolowski

Abstract read
In one paragraph

Article in PLOS global public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Rohan ArambepolaDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.ORCID https://orcid.org/0000-0003-2833-8786
Kathryn L SchaberDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.
Catherine SchluthDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.
Angkana T HuangDepartment of Genetics, Cambridge University, Cambridge, United Kingdom.
Alain B LabriqueDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.
Shruti H MehtaDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.
Sunil S SolomonDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.
Derek A T CummingsDepartment of Biology and the Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States of America.
Amy WesolowskiDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.

Funding

Human mobility models to forecast disease dynamics and the effectiveness of public health interventionsR01AI160780 · NIAID · JOHNS HOPKINS UNIVERSITY · PI CUMMINGS, DEREK A, WESOLOWSKI, AMY · 2021 to 2025
$3.3M
Disease Emergence and Elimination: Using Mobility Data to Inform Spatial Disease DynamicsDP2LM013102 · NLM · JOHNS HOPKINS UNIVERSITY · PI WESOLOWSKI, AMY · 2018 to 2018
$2.5M
NIAID NIH HHS R01 AI160780NLM NIH HHS DP2 LM013102
6 · The paper itself

Abstract

Human mobility patterns changed greatly due to the COVID-19 pandemic. Despite many analyses investigating general mobility trends, there has been less work characterising changes in mobility on a fine spatial scale and developing frameworks to model these changes. We analyse zip code-level within-city mobility data from 26 US cities between February 2 -August 31, 2020. We use Bayesian models to characterise the initial decrease in mobility and mobility patterns between June-August at this fine spatial scale. There were similar temporal trends across cities but large variations in the magnitude of mobility reductions. Long-distance routes and higher-income subscribers, but not age, were associated with greater mobility reductions. At the city level, mobility rates around early April, when mobility was lowest, and over summer showed little association with non-pharmaceutical interventions or case rates. Changes in mobility patterns lasted until the end of the study period, despite overall numbers of trips recovering to near baseline levels in many cities.

Identifiers

PMID37478056
PMCPMC10361529

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.