Cited by 60COVID-19 epidemiological studiesCOVID-19 Prevention and ImpactSARS-CoV-2 and COVID-19 Research

Did border closures slow SARS-CoV-2?

Open access
Shiraef, Mary A., Friesen, Paul, Feddern, Lukas, Weiss, Mark A., COBAP Team, Janabi, Hawraa Al, Beling, Elizabeth, Falcone, Jonathan +9 more·2022·Scientific Reports

Abstract

Despite the economic, social, and humanitarian costs of border closures, more than 1000 new international border closures were introduced in response to the 2020–2021 pandemic by nearly every country in the world. The objective of this study was to examine whether these border closures reduced the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Prior to 2020, the impacts of border closures on disease spread were largely unknown, and their use as a pandemic policy was advised against by international organizations. We tested whether they were helpful in reducing spread by using matching techniques on our hand-coded COVID Border Accountability Project (COBAP) Team database of international closures, converted to a time-series cross-sectional data format. We controlled for national-level internal movement restrictions (domestic lockdowns) using the Oxford COVID-19 Government Response Tracker (OxCGRT) time-series data. We found no evidence in favor of international border closures, whereas we found a strong association between national-level lockdowns and a reduced spread of SARS-CoV-2 cases. More research must be done to evaluate the byproduct effects of closures versus lockdowns as well as the efficacy of other preventative measures introduced at international borders.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41598-022-05482-7

Cites

28 of 28 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. [4]World Health Organization (WHO). Non-pharmaceutical public health measures for mitigating the risk and impact of epidemic and pandemic influenza. https://apps.who.int/iris/bitstream/handle/10665/329439/WHO-WHE-IHM-GIP-2019.1-eng.pdf (2020). 42118904
  2. [28]Rubin DB Matched Sampling for Causal Effects 2006 Cambridge University Press

Cited by

Sorted by citations

Authors
Shiraef, M. A., Friesen, P., Feddern, L., Weiss, M. A., COBAP Team, Janabi, H. A., Beling, E., Falcone, J., Feddern, L., Hirst, C., Murphy, N., Stifel, E., Straight, E., Tao, D., Tutaj, E., Weiss, M. A., & Shiraef, M. A.
Affiliations
  • University of Notre Dame (US)
  • Emory University (US)
  • University of Waterloo (CA)
  • Vanderbilt University (US)
Corresponding author
Friesen, Paul
Venue
Scientific Reports
Published
2022-02-01
Volume
12
Issue
1
Pages
1709-1709
ISSN
2045-2322 (ISSN-L)
OpenAlex
W4210415025
Cited by
60
References
28
Citations by year
2026: 2 · 2025: 11 · 2024: 17 · 2023: 19 · 2022: 11
FWCI
8.58
Type
journal article
Language
en
Open access
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • KOPS (University of Konstanz) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • University of Notre Dame
  • University of Waterloo
  • Kellogg Institute for International Studies, University of Notre Dame
Topics
  • Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
  • Social Sciences › Sociology and Political Science › COVID-19 Prevention and Impact
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
Keywords
SARS-CoV-2, COVID-19 pandemic, SARS-CoV-2 transmission, lockdown, non-pharmaceutical interventions, pandemic policy
MeSH terms
COVID-19, SARS-CoV-2, Emigration and Immigration, Humans, Quarantine, Pandemics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review