Evidence map›Paper›PMID 38602941›Full record

ArticleTransportation research record2023

Transportation as a Disease Vector in COVID-19: Border Mobility and Disease Spread.

Okan Gurbuz, Rafael M Aldrete, David Salgado, Tugba Mehmetoglu Gurbuz

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 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.

  1. Article
  2. Article
  3. Article
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

4 authors.

Okan GurbuzTexas A&M Transportation Institute, El Paso, TX.ORCID https://orcid.org/0000-0002-4258-9445
Rafael M AldreteTexas A&M Transportation Institute, El Paso, TX.ORCID https://orcid.org/0000-0003-2826-8335
David SalgadoTexas A&M Transportation Institute, El Paso, TX.
Tugba Mehmetoglu GurbuzUT Southwestern Medical Center, Dallas, TX.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

More than a year after COVID-19 was declared a pandemic by the World Health Organization, the U.S.A. and Mexico rank first and fourth, respectively, with regard to the number of deaths. From March 2020, nonessential travelers were not allowed to cross the border into the U.S.A. from Mexico via international land ports of entry, which resulted in a more than 50% decrease in the number of people crossing the border. However, border communities still face a higher number of cases and faster community spread compared with those without international land ports of entry. This paper established an econometric model to understand the effects of cross-border mobility and other socioeconomic parameters on the speed of spread. The model was developed at the U.S. county level using data from all 3,141 counties in the U.S.A. Additionally, a follow-up U.S. county comparative analysis was developed to examine the significance of having a border crossing between the U.S.A. and Mexico for U.S. counties. The findings of the analysis revealed that the variables having a significant effect are as follows: population density; number of people per household; population in the 15-65 age group; median household income; mask use; number of visits to transit stations; number of visits to workplace; overall mobility; and having a border crossing to Mexico within county limits. The comparative analysis found that U.S. counties with border crossings have an average of 123 cases per 1,000 population whereas their counterparts without border crossings only have 90 cases per 1,000 population.

Indexed as

border crossingborder mobilityCOVID-19infectious disease spread

Identifiers

PMID38602941
PMCPMC10008995

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.