ArticleJournal of transport & health2021
Spatial association of mobility and COVID-19 infection rate in the USA: A county-level study using mobile phone location data.
Article in Journal of transport & health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review.The Science of the total environment · 2022Pooled it
- Characterizing US Spatial Connectivity and Implications for Geographical Disease Dynamics and Metapopulation Modeling: Longitudinal Observational Study.JMIR public health and surveillance · 2025Observational
- Challenges and opportunity in mobility among older adults - key determinant identification.BMC geriatrics · 2023Review
- The role of the California tier system in controlling population mobility during the COVID-19 pandemic.BMC public health · 2023Article
- The Role of Functional Urban Areas in the Spread of COVID-19 Omicron (Northern Spain).Journal of urban health : bulletin of the New York Academy of Medicine · 2023Article
- Article
- Using Mobile Phone Data to Assess Socio-Economic Disparities in Unhealthy Food Reliance during the COVID-19 Pandemic.Health data science · 2023Article
- Did the COVID-19 vaccine rollout impact transportation demand? A case study in New York City.Journal of transport & health · 2023Article
- Changes in mobility amid the COVID-19 pandemic in Sapporo City, Japan: An investigation through the relationship between spatiotemporal population density and urban facilities.Transportation research interdisciplinary perspectives · 2023Article
- Investigating the relationships between concentrated disadvantage, place connectivity, and COVID-19 fatality in the United States over time.BMC public health · 2022Article
- Green spaces, especially nearby forest, may reduce the SARS-CoV-2 infection rate: A nationwide study in the United States.Landscape and urban planning · 2022Article
- Machine learning based regional epidemic transmission risks precaution in digital society.Scientific reports · 2022Article
- Data analytics during pandemics: a transportation and location planning perspective.Annals of operations research · 2022Article
- Modeling the effects of the governmental responses to COVID-19 on transit demand: The case of Athens, Greece.Case studies on transport policy · 2022Article
- Which factors influence mobility change during COVID-19 in Germany? Evidence from German county data.Regional science policy & practice · 2022Article
- Public health research using cell phone derived mobility data in sub-Saharan Africa: Ethical issues.South African journal of scienceArticle
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1 author.
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No grant is acknowledged in the PubMed record.
Abstract
introductionHuman mobility has been a central issue in the discussion from the beginning of COVID-19. While the body of literature on the relationship of COVID transmission and mobility is large, studies mostly captured a relatively short timeframe. Moreover, spatial non-stationarity has garnered less attention in these explorative models. Therefore, the major concern of this study is to see the relationship of mobility and COVID on a broader temporal scale and after mitigating this methodological gap.
objectiveIn response to this concern, this study first explores the spatiotemporal pattern of mobility indicators. Secondly, it attempts to understand how mobility is related to COVID infection rate and how this relationship has been changed over time and space after controlling several sociodemographic characteristics, spatial heterogeneity, and policy-related changes during different phases of Coronavirus. DATA AND
methodThis study uses GPS-based mobility data for a wider time frame of six months (March 20-August'20) divided into four tiers and carries analysis for all the US counties (N = 3142). Space-time cube is used to generate the spatiotemporal pattern. For the second objective, Ordinary Least Square (OLS), Spatial Error Model (SEM), and Geographically Weighted Regression (GWR) were used.
resultThe spatial-temporal pattern suggests that the trip rate, out-of-county trip rate, and miles/person traveled were mostly plummeted till the first wave reached its peak, and subsequently, all of these mobility matrices started to rise. From spatial models, infection rates were found negatively correlated with miles traveled and out-of-county trips. Highly COVID infected areas mostly had more people working from home, low percentages of aged people and educated people, and high percentages of poor people.
conclusionThis study, with necessary policy implications, provides a comprehensive understanding of the shifting pattern of mobility and COVID. Spatial models outperform OLS with better fits and non-clustered residuals.
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