Evidence map›Paper›PMID 36685313›Full record

ArticleTransportation research. Part A, Policy and practice2023

Analyzing multiple COVID-19 outbreak impacts: A case study based on Chinese national air passenger flow.

Jinghua Wang, Zhao Zhang, Guangquan Lu, Bin Yu, Chengyu Zhan, Jingsong Cai

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Transportation research. Part A, Policy and practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
3.3field-weighted citation impact, top 8% of its field
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

1 citing paper in PubMed, 15 citations in OpenAlex.

  1. 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

6 authors at 1 institution in 1 country.

Jinghua WangSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, China.
Zhao ZhangSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, China.
Guangquan LuSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, China.
Bin YuSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, China.
Chengyu ZhanSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, China.
Jingsong CaiSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, China.
Beihang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The spread of COVID-19 results in a significant drop in traffic levels worldwide. Quantifying the impact of multiple COVID-19 outbreaks on traffic systems is critical to developing differentiated policies in the future. This paper proposes a novel COVID-19 multiple outbreak analysis method (NCMOA), dividing the impact scope and degree under multiple COVID-19 disturbances, and using the recovery rate and accumulated loss to quantify the impacts on air passenger flow. A case study based on Chinese national air traffic flow is executed, and the recovery patterns and the differentiated disturbances are analyzed. Results show that air passenger flow recovers with a similar pattern after the first outbreak, and subsequent outbreaks cause local effects and cannot affect the overall recovery pattern. Further, the heterogeneous influence factors and trends on the

Indexed as

Accumulated lossAir passenger flowCOVID-19Multiple disturbancesRecovery rate

Identifiers

PMID36685313
PMCPMC9842631
OpenAlexW4316810301

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.