Evidence map›Paper›PMID 32904431›Full record

ArticleComputational mechanics2020

Is it safe to lift COVID-19 travel bans? The Newfoundland story.

Kevin Linka, Proton Rahman, Alain Goriely, Ellen Kuhl

Abstract read
In one paragraph

Article in Computational mechanics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 3 pooled it
–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

36 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Key questions for modelling COVID-19 exit strategies.Proceedings. Biological sciences · 2020
    Pooled it
  4. Article
  5. Article
  6. Travel bubble policies for low-risk air transport recovery during pandemics.Risk analysis : an official publication of the Society for Risk Analysis · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Article
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  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Determining travel fluxes in epidemic areas.PLoS computational biology · 2021
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Kevin LinkaDepartment of Mechanical Engineering, Stanford University, Stanford, CA USA.
Proton RahmanDepartment of Medicine, Memorial University of Newfoundland, St. John's, Canada.
Alain GorielyMathematical Institute, University of Oxford, Oxford, UK.
Ellen KuhlDepartment of Mechanical Engineering, Stanford University, Stanford, CA USA.

Funding

Multi-Scale Laws of Myocardial Growth and RemodelingU01HL119578 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GUCCIONE, JULIUS MATTEO, KASSAB, GHASSAN S · 2014 to 2018
$3.8M
NHLBI NIH HHS U01 HL119578
6 · The paper itself

Abstract

A key strategy to prevent a local outbreak during the COVID-19 pandemic is to restrict incoming travel. Once a region has successfully contained the disease, it becomes critical to decide when and how to reopen the borders. Here we explore the impact of border reopening for the example of Newfoundland and Labrador, a Canadian province that has enjoyed no new cases since late April, 2020. We combine a network epidemiology model with machine learning to infer parameters and predict the COVID-19 dynamics upon partial and total airport reopening, with perfect and imperfect quarantine conditions. Our study suggests that upon full reopening, every other day, a new COVID-19 case would enter the province. Under the current conditions, banning air travel from outside Canada is more efficient in managing the pandemic than fully reopening and quarantining 95% of the incoming population. Our study provides quantitative insights of the efficacy of travel restrictions and can inform political decision making in the controversy of reopening.

Indexed as

COVID-19EpidemiologyMachine learningReproduction numberSEIR model

Identifiers

PMID32904431
PMCPMC7456209

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.