Evidence map›Paper›PMID 37065273›Full record

ArticleTransport policy2023

Interaction between travel restriction policies and the spread of COVID-19.

Xin Meng, Mingxue Guo, Ziyou Gao, Liujiang Kang

Abstract read
In one paragraph

Article in Transport policy, 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.

Xin MengSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, China.
Mingxue GuoSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, China.
Ziyou GaoSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, China.
Liujiang KangSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To investigate the interaction between travel restriction policies and the spread of COVID-19, we collected data on human mobility trends, population density, Gross Domestic Product (GDP) per capita, daily new confirmed cases (or deaths), and the total confirmed cases (or deaths), as well as governmental travel restriction policies from 33 countries. The data collection period was from April 2020 to February 2022, resulting in 24,090 data points. We then developed a structural causal model to describe the causal relationship between these variables. Using the Dowhy method to solve the developed model, we found several significant results that passed the refutation test. Specifically, travel restriction policies played an important role in slowing the spread of COVID-19 until May 2021. International travel controls and school closures had an impact on reducing the spread of the pandemic beyond the impact of travel restrictions. Additionally, May 2021 marked a turning point in the spread of COVID-19 as it became more infectious, but the mortality rate gradually decreased. The impact of travel restriction policies on human mobility and the pandemic diminished over time. Overall, the cancellation of public events and restrictions on public gatherings were more effective than other travel restriction policies. Our findings provide insights into the effects of travel restriction policies and travel behavioral changes on the spread of COVID-19, while controlling for informational and other confounding variables. This experience can be applied in the future to respond to emergent infectious diseases.

Indexed as

COVID-19Human mobilityStructural causal modelTravel restriction policies

Identifiers

PMID37065273
PMCPMC10086066

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.