Evidence map›Paper›PMID 36694823›Full record

ArticleTransportation research interdisciplinary perspectives2023

Local travel behaviour under continuing COVID-19 waves- A proxy for pandemic fatigue?

Nan Zhang, Tingrui Hu, Shujia Shang, Shiyao Zhang, Wei Jia, Jinhang Chen, Zixuan Zhang, Boni Su, Zhenyu Wang, Reynold Cheng and 1 more

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In one paragraph

Article in Transportation research interdisciplinary perspectives, 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
–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

1 citing paper in PubMed.

  1. Heterogeneous risk tolerance, in-groups, and epidemic waves.Frontiers in applied mathematics and statistics · 2024
    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

11 authors.

Nan ZhangBeijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China.
Tingrui HuBeijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China.
Shujia ShangBeijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China.
Shiyao ZhangThe Sifakis Research Institute for Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen 518055, China.
Wei JiaDepartment of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China.
Jinhang ChenFaculty of Information Technology, Beijing University of Technology, Beijing, China.
Zixuan ZhangBeijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China.
Boni SuChina Electric Power Planning & Engineering Institute, Beijing, China.
Zhenyu WangCollege of Economics and Management, Beijing University of Technology, Beijing, China.
Reynold ChengDepartment of Computer Science, The University of Hong Kong, Hong Kong SAR, China.
Yuguo LiDepartment of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 continues to threaten the world. Relaxing local travel behaviours on preventing the spread of COVID-19, may increase the infection risk in subsequent waves of SARS-CoV-2 transmission. In this study, we analysed changes in the travel behaviour of different population groups (adult, child, student, elderly) during four pandemic waves in Hong Kong before January 2021, by 4-billion second-by-second smartcard records of subway. A significant continuous relaxation in human travel behaviour was observed during the four waves of SARS-CoV-2 transmission. Residents sharply reduced their local travel by 51.9%, 50.1%, 27.6%, and 20.5% from the first to fourth pandemic waves, respectively. The population flow in residential areas, workplaces, schools, shopping areas, amusement areas and border areas, decreased on average by 30.3%, 33.5%, 41.9%, 58.1%, 85.4% and 99.6%, respectively, during the pandemic weeks. We also found that many other cities around the world experienced a similar relaxation trend in local travel behaviour, by comparing traffic congestion data during the pandemic with data from the same period in 2019. The quantitative pandemic fatigue in local travel behaviour could help governments partially predicting personal protective behaviours, and thus to suggest more accurate interventions during subsequent waves, especially for highly infectious virus variants such as Omicron.

Indexed as

COVID-19Local travel behaviourPublic transportSubwayTraffic congestion

Identifiers

PMID36694823
PMCPMC9850857

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.