Evidence map›Paper›PMID 35484268›Full record

ArticleScientific reports2022

Framework for assessing and easing global COVID-19 travel restrictions.

Thien-Minh Le, Louis Raynal, Octavious Talbot, Hali Hambridge, Christopher Drovandi, Antonietta Mira, Kerrie Mengersen, Jukka-Pekka Onnela

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Thien-Minh LeDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. thle@hsph.harvard.edu.
Louis RaynalDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Octavious TalbotDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Hali HambridgeDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Christopher DrovandiSchool of Mathematical Sciences, Faculty of Science, Queensland University Technology, Brisbane, Australia.
Antonietta MiraUniversità della Svizzera Italiana, Lugano, Switzerland.
Kerrie MengersenSchool of Mathematical Sciences, Faculty of Science, Queensland University Technology, Brisbane, Australia.ORCID 0000-0001-8625-9168
Jukka-Pekka OnnelaDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. onnela@hsph.harvard.edu.

Funding

Bridging Statistical Inference and Mechanistic Network Models for HIV/AIDSR01AI138901 · NIAID · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Ravi Goyal, Jukka-Pekka Onnela · 2019 to 2026
$3.4M
NIAID NIH HHS AI138901NIAID NIH HHS R01 AI138901Swiss National Science Foundation 105218_163196
6 · The paper itself

Abstract

During the COVID-19 pandemic, many countries implemented international travel restrictions that aimed to contain viral spread while still allowing necessary cross-border travel for social and economic reasons. The relative effectiveness of these approaches for controlling the pandemic has gone largely unstudied. Here we developed a flexible network meta-population model to compare the effectiveness of international travel policies, with a focus on evaluating the benefit of policy coordination. Because country-level epidemiological parameters are unknown, they need to be estimated from data; we accomplished this using approximate Bayesian computation, given the nature of our complex stochastic disease transmission model. Based on simulation and theoretical insights we find that, under our proposed policy, international airline travel may resume up to 58% of the pre-pandemic level with pandemic control comparable to that of a complete shutdown of all airline travel. Our results demonstrate that global coordination is necessary to allow for maximum travel with minimum effect on viral spread.

Indexed as

COVID-19Influenza, HumanBayes TheoremHumansPandemicsTravel

Identifiers

PMID35484268
PMCPMC9049014

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.