Evidence map›Paper›PMID 37153196›Full record

ArticleTransportation research record2023

Interactive COVID-19 Mobility Impact and Social Distancing Analysis Platform.

Lei Zhang, Aref Darzi, Sepehr Ghader, Michael L Pack, Chenfeng Xiong, Mofeng Yang, Qianqian Sun, Aliakbar Kabiri, Songhua Hu

Abstract read
In one paragraph

Article in Transportation research record, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing 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

46 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Lei ZhangMaryland Transportation Institute, Department of Civil and Environmental Engineering, University of Maryland, College Park, MD.ORCID https://orcid.org/0000-0002-3372-6321
Aref DarziMaryland Transportation Institute, Department of Civil and Environmental Engineering, University of Maryland, College Park, MD.ORCID https://orcid.org/0000-0003-2558-5570
Sepehr GhaderMaryland Transportation Institute, Department of Civil and Environmental Engineering, University of Maryland, College Park, MD.ORCID https://orcid.org/0000-0003-1938-7914
Michael L PackCenter for Advanced Transportation Technology Laboratory, University of Maryland, College Park, MD.
Chenfeng XiongMaryland Transportation Institute, Department of Civil and Environmental Engineering, University of Maryland, College Park, MD.ORCID https://orcid.org/0000-0003-4237-1750
Mofeng YangMaryland Transportation Institute, Department of Civil and Environmental Engineering, University of Maryland, College Park, MD.ORCID https://orcid.org/0000-0002-0525-7978
Qianqian SunMaryland Transportation Institute, Department of Civil and Environmental Engineering, University of Maryland, College Park, MD.ORCID https://orcid.org/0000-0003-3684-4603
Aliakbar KabiriMaryland Transportation Institute, Department of Civil and Environmental Engineering, University of Maryland, College Park, MD.
Songhua HuMaryland Transportation Institute, Department of Civil and Environmental Engineering, University of Maryland, College Park, MD.ORCID https://orcid.org/0000-0002-0731-3080

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population data, to produce a COVID-19 impact analysis platform that can inform users about the effects of COVID-19 spread and government orders on mobility and social distancing. The platform is being updated daily, to continuously inform decision-makers about the impacts of COVID-19 on their communities, using an interactive analytical tool. The research team has processed anonymized mobile device location data to identify trips and produced a set of variables, including social distancing index, percentage of people staying at home, visits to work and non-work locations, out-of-town trips, and trip distance. The results are aggregated to county and state levels to protect privacy, and scaled to the entire population of each county and state. The research team is making their data and findings, which are updated daily and go back to January 1, 2020, for benchmarking, available to the public to help public officials make informed decisions. This paper presents a summary of the platform and describes the methodology used to process data and produce the platform metrics.

Identifiers

PMID37153196
PMCPMC10152223

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.