Did border closures slow SARS-CoV-2?
Open accessAbstract
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.
Integrity signals
Registry
Cites
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.
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?
A 2022 study found no evidence that border closures reduced the spread of COVID-19.
“However, a 2022 study found no evidence that border closures reduced the spread of COVID-19.110”
From The Lancet Commission on lessons for the future from the COVID-19 pandemic (2022) · cites this paper as [110]
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
An interactive web-based dashboard to track COVID-19 in real time
Dong, E., Du, H., & Gardner, L. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30120-1 · Cited by 10,048 · see citing works
A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker)
Hale, T., Angrist, N., Goldszmidt, R., Kira, B., Petherick, A., Phillips, T., Webster, S., Cameron-Blake, E., Hallas, L., Majumdar, S., et al. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01079-8 · Cited by 4,578 · see citing works
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
Ho, D. E., Imai, K., King, G., & Stuart, E. (2007) · Political Analysis · doi:10.1093/pan/mpl013 · Cited by 4,411 · see citing works
Wu, J. T., Leung, K., & Leung, G. M. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30260-9 · Cited by 4,389 · see citing works
The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak
Chinazzi, M., Davis, J. T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., Pastore y Piontti, A., Mu, K., Rossi, L., Sun, K., et al. (2020) · Science · doi:10.1126/science.aba9757 · Cited by 3,694 · see citing works
Early dynamics of transmission and control of COVID-19: a mathematical modelling study
Kucharski, A. J., Russell, T., Diamond, C., Liu, Y., Edmunds, J., Funk, S., Eggo, R. M., Sun, F., Jit, M., Munday, J. D., et al. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30144-4 · Cited by 2,675 · see citing works
Tian, H., Liu, Y., Li, Y., Wu, C.-H., Chen, B., Kraemer, M. U. G., Li, B., Cai, J., Xu, B., Yang, Q., et al. (2020) · Science · doi:10.1126/science.abb6105 · Cited by 2,010 · see citing works
The effect of large-scale anti-contagion policies on the COVID-19 pandemic
Hsiang, S., Allen, D., Annan-Phan, S., Bell, K., Bolliger, I. W., Chong, T., Druckenmiller, H., Huang, L. Y., Hultgren, A., Krasovich, E., et al. (2020) · Nature · doi:10.1038/s41586-020-2404-8 · Cited by 1,378 · see citing works
Cowling, B. J., Ali, S. T., Ng, T. W. Y., Tsang, T. K., Li, J. C. M., Fong, M. W., Liao, Q., Kwan, M. Y., Lee, S. L., Chiu, S. S., et al. (2020) · The Lancet Public Health · doi:10.1016/s2468-2667(20)30090-6 · Cited by 1,258 · see citing works
World Bank World Development Indicators
Ott, J. C. (2014) · Springer eBooks · doi:10.1007/978-94-007-0753-5_3278 · Cited by 680 · see citing works
On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data
Imai, K., & Kim, I. S. (2021) · Political Analysis · doi:10.1017/pan.2020.33 · Cited by 623 · see citing works
Matched Sampling for Causal Effects: Contributor Acknowledgments
Rubin, D. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810725 · Cited by 287 · see citing works
Li, F., Li, Y.-Y., Liu, M.-J., Fang, L.-Q., Dean, N. E., Wong, G., Yang, X.-B., Longini, I. M., Halloran, M. E., Wang, H.-J., et al. (2021) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30981-6 · Cited by 242 · see citing works
Anzai, A., Kobayashi, T., Linton, N. M., Kinoshita, R., Hayashi, K., Suzuki, A., Yang, Y., Jung, S.-M., Miyama, T., Akhmetzhanov, A. R., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9020601 · Cited by 200 · see citing works
Hu, S., Wang, W., Wang, Y., Litvinova, M., Luo, K., Ren, L., Sun, Q., Chen, X., Ge, Z., Li, J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21710-6 · Cited by 166 · see citing works
Grépin, K. A., Ho, T. L., Liu, Z., Marion, S., Piper, J., Worsnop, C. Z., & Lee, K. (2021) · BMJ Global Health · doi:10.1136/bmjgh-2020-004537 · Cited by 152 · see citing works
Covidfencing effects on cross-border deterritorialism: the case of Europe
Medeiros, E., Ramírez, M. G., Ocskay, G., et al. (2020) · European Planning Studies · doi:10.1080/09654313.2020.1818185 · Cited by 134 · see citing works
Coppedge, M. (2021) · Varieties of Democracy · doi:10.23696/vdemds21 · Cited by 130 · see citing works
Mateus, A. L., Otete, H. E., Beck, C., et al. (2014) · Bulletin of the World Health Organization · doi:10.2471/blt.14.135590 · Cited by 122 · see citing works
Ryu, S., Gao, H., Wong, J. Y., Shiu, E. Y. C., Xiao, J., Fong, M. W., & Cowling, B. J. (2020) · Emerging infectious diseases · doi:10.3201/eid2605.190993 · Cited by 110 · see citing works
The Covid-19 crisis: the end of a borderless Europe?
Opiłowska, E. (2020) · European Societies · doi:10.1080/14616696.2020.1833065 · Cited by 97 · see citing works
Borders resurgent: towards a post-Covid-19 global border regime?
Radil, S. M., Pinos, J. C., & Ptak, T. (2020) · Space and Polity · doi:10.1080/13562576.2020.1773254 · Cited by 87 · see citing works
Bou-Karroum, L., Khabsa, J., Jabbour, M., Hilal, N., Haidar, Z., Khalil, P. A., Abdul-Khalek, R. A., Assaf, J., Honein-AbouHaidar, G. N., Samra, C. A., et al. (2021) · Journal of Infection · doi:10.1016/j.jinf.2021.07.017 · Cited by 69 · see citing works
Travel restrictions and SARS-CoV-2 transmission: an effective distance approach to estimate impact
Shi, S., Tanaka, S., Ueno, R., Gilmour, S., Tanoue, Y., Kawashima, T., Nomura, S., Eguchi, A., Miyata, H., & Yoneoka, D. (2020) · Bulletin of the World Health Organization · doi:10.2471/blt.20.255679 · Cited by 44 · see citing works
Aravindakshan, A., Boehnke, J., Gholami, E., & Nayak, A. (2020) · Scientific Reports · doi:10.1038/s41598-020-76244-6 · Cited by 37 · see citing works
Shiraef, M. A., Hirst, C., Weiss, M. A., et al. (2021) · Scientific Data · doi:10.1038/s41597-021-01031-5 · Cited by 18 · see citing works
Self-citationShiraef, M. A., Hirst, C., Weiss, M. A., et al. (2020) · Harvard Dataverse · doi:10.7910/dvn/u6djac · Cited by 3 · see citing works
Self-citationReplication Materials: Did Border Closures Slow SARS-CoV-2?
Shiraef, M. A., Friesen, P. H., Feddern, L., et al. (2021) · Harvard Dataverse · doi:10.7910/dvn/d1scuk · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- [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
- [28]Rubin DB Matched Sampling for Causal Effects 2006 Cambridge University Press
Cited by
Sorted by citations
The Lancet Commission on lessons for the future from the COVID-19 pandemic
Sachs, J. D., Karim, S. S. A., Aknin, L., Allen, J., Brosbøl, K., Colombo, F., Barron, G. C., Espinosa, M. F., Gaspar, V., Gaviria, A., et al. (2022) · The Lancet · doi:10.1016/s0140-6736(22)01585-9 · Cited by 842 · see citing works
- 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
- ORCID
- Friesen, Paul
- Venue
- Scientific Reports
- Published
- 2022-02-01
- Volume
- 12
- Issue
- 1
- Pages
- 1709-1709
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC8807811
- 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
- gold
- doi.org
- 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