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Review
. 2022 Feb 1;12(1):1709.
doi: 10.1038/s41598-022-05482-7.

Did border closures slow SARS-CoV-2?

Mary A Shiraef  1 , Paul Friesen  2 , Lukas Feddern  3   4 , Mark A Weiss  5 ; COBAP Team
Collaborators, Affiliations
Review

Did border closures slow SARS-CoV-2?

Mary A Shiraef et al. Sci Rep..

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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Descriptive data of border closure policies and new Covid-19 cases from January, 2020 until April, 2021.
Figure 2
Figure 2
This figure illustrates the measured effects of a domestic lockdown on the rate of change of new cases per capita after IHST (y-axis). The estimates in grey were generated with neither matching nor refinement, while the green estimates were generated with matching and refinement, both displayed with 95% confidence intervals. The refinement strategy selected is covariate balancing propensity score matching. The period includes nine weeks, three prior to the lockdown, the week of the lockdown, and five following the lockdown.
Figure 3
Figure 3
This figure shows the measured effects of complete closures on the rate of change of new cases per capita after IHST (y-axis). The estimates in grey were generated with neither matching nor refinement, while the blue estimates were generated with matching and refinement, both displayed with 95% confidence intervals. The optimal refinement strategy selected is propensity score matching. The period includes nine weeks: three prior to the border closure, the week of the closure, and five following the closure.
Figure 4
Figure 4
This figure shows the measured effects of partial closures on the rate of change of new cases per capita after IHST (y-axis). The estimates in grey were generated with neither matching nor refinement, while the red estimates were generated with matching and refinement, both displayed with 95% confidence intervals. The optimal refinement strategy selected is covariate balancing propensity score matching. The period includes nine weeks: three prior to the border closure, the week of the closure, and five following the closure.
Figure 5
Figure 5
The measured effects of complete closure on the rate of change of new cases per capita after IHST (y-axis) for a sub-set of 89 island nations. The estimates are shown with both no matching or refinement in grey, and matching and refinement in blue, calculated with 95% confidence intervals. The period includes nine weeks: three prior to the lockdown, the week of the lockdown, and five following the lockdown.
Figure 6
Figure 6
The measured effects of partial closure on the rate of change of new cases per capita after IHST (y-axis) for a sub-set of 89 island nations. The estimates are shown with both no matching or refinement in grey, and matching and refinement in red, calculated with 95% confidence intervals. The period includes nine weeks: three prior to the lockdown, the week of the lockdown, and five following the lockdown.
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