Evidence map›Paper›PMID 37325801›Full record

ArticleTransportation research. Part E, Logistics and transportation review2023

Impacts of household vulnerability on hurricane logistics evacuation under COVID-19: The case of U.S. Hampton Roads.

Rafael Diaz, Beatriz Acero, Joshua G Behr, Nicole S Hutton

Abstract read
In one paragraph

Article in Transportation research. Part E, Logistics and transportation review, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Rafael DiazSchool of Cybersecurity, Department of Engineering Management & Systems Engineering, Virginia Modeling and Simulation Center, Old Dominion University, United States.
Beatriz AceroVirginia Modeling and Simulation Center, Old Dominion University, United States.
Joshua G BehrVirginia Modeling and Simulation Center, Old Dominion University, United States.
Nicole S HuttonDepartment of Political Science and Geography, College of Arts & Letters, Old Dominion University, 7012 Batten Arts and Letters, Norfolk, VA 23529, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Historical data suggest that when a severe tropical storm or hurricane impacts a community, the vulnerable segment of the population suffers the most severe consequences. With an increased aging population, it is crucial to understand how vulnerability alters evacuation behavior. Emergent variables such as fear of COVID-19 require additional exploration. People afraid of COVID-19 exposure may refuse to evacuate, exposing themselves unnecessarily. Differentiation is critical to evacuation logistics since it is needed to determine what proportion would stay in a local shelter, public or other, rather than evacuating or staying in their home and guide the logistics resource allocation process. This research uses data from a web and phone survey conducted in the Hampton Roads area of U.S. Virginia, with 2,200 valid responses to analyze the influence of social and demographic vulnerability factors and risk perception on evacuation decisions. This research contributes to the existing literature by developing a multinomial order logit model based on vulnerability factors and intended evacuation decisions, including staying at home, looking for a shelter, or leaving the Hampton Roads area. Findings show that race and risk perception are the variables that influence the decision-making process the most. Fear of COVID-19 transmission is also associated with an increased likelihood of leaving homes during evacuation. The variations in findings from previous studies are discussed regarding their implications for logistics emergency managers.

Indexed as

COVID-19 pandemicDecision calculusEvacuation behaviorHurricaneVulnerable population

Identifiers

PMID37325801
PMCPMC10234347

What OpenQuestion holds

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Registered trials

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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.