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Cochrane Rapid Review examines international travel-related control measures to contain the COVID-19 pandemic
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Updated review: Can international travel-related control measures contain the spread of the COVID-19 pandemic?
May 10: updated with visual abstract
What are international travel-related control measures?
International travel control measures are methods to manage international travel to contain the spread of COVID-19. Measures include:
- closing international borders to stop travellers crossing from one country to another;
- restricting travel to and from certain countries, particularly those with high infection levels;
- screening or testing travellers entering or leaving a country if they have symptoms or have been in contact with an infected person;
- quarantining newly arrived travellers from another country, that is, requiring travellers to stay at home or in a specific place for a certain time.
What did the authors want to find out?
They wanted to find out how effective international travel-related control measures are in containing the COVID-19 pandemic.
What they did
The authors searched for studies on the effects of these measures on the spread of COVID-19. Studies had to report how many cases these measures prevented or detected, or whether they changed the course of the pandemic. The studies could include people of any age, anywhere. They could be of any design, including those that used ‘real-life’ data (observational studies) or hypothetical data from computer-generated simulations (modelling studies).
This is the first update of this review. This update includes only studies on COVID-19, published up to 13 November 2020.
![travel control](https://www.cochrane.org/sites/default/files/public/uploads/review_2.png)
What they found
The authors found 62 studies. Most (49 studies) were modelling studies; only 13 used real-life data (observational studies). Studies took place across the world and at different times during the pandemic. Levels of COVID-19 within countries varied.
Most studies compared current travel-related control measures with no travel-related controls. However, some modelling studies also compared current measures against possible measures, for example, to see what might happen if controls were more or less relaxed or were combined with other measures.
![travel control Visual Abstract](https://www.cochrane.org/sites/default/files/public/uploads/travel_control_measures_-_cd013717.png)
View this visual abstract is a larger size, download, or print Learn more about the Emory medical student visual abstracts View the visual abstract in Spanish
Main results
Travel restrictions reducing or stopping cross-border travel (31 modelling studies)
- Cases avoided (22 studies): most studies reported that controlling cross-border travel reduced cases in the community (11 studies) and reduced the number of imported or exported cases (9 studies). However, results from individual studies differed, with some studies showing that cross-border controls had little or no effect and others showing a large effect. Due to these differences, the authors are very uncertain about these results.
- Number of days outbreak was delayed (12 studies): overall results showed that cross-border travel controls slowed the spread of COVID-19. However, results from individual studies varied from a delay of less than one day to a delay of 85 days. The authors are very uncertain about these results.
Screening at borders (13 modelling studies and 13 observational studies)
These measures assessed screening or testing people with symptoms or who had been exposed to COVID-19, before or after they travelled.
- Screening: all studies found that screening reduced imported or exported cases, delayed outbreaks and reduced the number or proportion of cases detected. Observational studies reported a wide range of cases detected, from 0% to 100%. This variation might be due to the way screening was carried out or to the specific screening measure.
- Testing: studies reported that testing travellers reduced imported or exported cases, and cases detected. Observational studies reported that the proportion of cases detected varied from 58% to 90%. This variation might be due to the timing of testing.
Quarantine (12 modelling studies)
Quarantine may lead to fewer imported or exported cases, delayed outbreaks and a reduced number or proportion of cases detected, but effects of quarantine varied widely depending on the length of time for which people quarantined and how well they followed the rules.
Quarantine and screening at borders (7 modelling studies and 4 observational studies)
Quarantine combined with screening may delay outbreaks and reduce the number or proportion of cases detected compared to quarantine alone.
![travel control](https://www.cochrane.org/sites/default/files/public/uploads/covid_dta_3.png)
How reliable are these results?
The author team's confidence in these results is limited. Most studies were based on mathematical predictions (modelling), so their is a lack real-life evidence. Further, the authors were not confident that models used correct assumptions, so confidence in the evidence on travel restrictions and quarantine, in particular, is very low. Some studies were published quickly online as ‘preprints’. Preprints do not undergo the normal rigorous checks of published studies, and the authors are not certain how reliable they are. Also, the studies were very different from each other and their results varied according to the specification of the measure (e.g. the type of screening approach), how it was put into practice and enforced, the amount of cross-border travel, levels of community transmission and other types of national measures to control the pandemic.
What this means
Overall, international travel-related control measures may help to limit the spread of COVID-19 across national borders. Restricting cross-border travel can be a helpful measure. Screening travellers for symptoms at points of entry or exit is likely to miss many cases; testing may be more effective but may also miss cases. Quarantine that lasts at least 10 days can prevent travellers spreading COVID-19 and may be more effective if combined with another measure such as testing, and if people follow the rules.
Future research needs to be reported better, more studies should focus on real-life evidence, and should assess potential benefits and risks of travel-related control measures to individuals and society as a whole.
Jacob Burns lead author of this update explains, "In this update we identified a much expanded evidence base related to international travel control measures to contain the COVID-19 pandemic, with 38 additional studies focusing on COVID-19 identified. Many of the studies were similar with regard to scope and methods, and overall the conclusions of the updated review remain largely the same. Some aspects of the evidence base, however, were improved - for example, we identified studies from further parts of the world that were not represented in the original review, including African and Eastern Mediterranean regions. Additionally, we identified more studies evaluating entry and/or exit screening measures at real-world ports of entry."
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International travel controls during the COVID-19 pandemic
What you should know about this indicator.
Possible categories:
- 0: no restrictions
- 1: screening arrivals
- 2: quarantine arrivals from some or all regions
- 3: ban arrivals from some regions
- 4: ban on all regions or total border closure
Related research and writing
![travel control travel control](https://ourworldindata.org/wp-content/uploads/2021/01/covid-policy-responses.png)
Policy Responses to the Coronavirus Pandemic
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COVID-19: International and Domestic Travel
Sources and processing, this data is based on the following sources, blavatnik school of government, university of oxford – government response tracker (oxcgrt).
The Oxford Covid-19 Government Response Tracker (OxCGRT) collected information on which pandemic response measures were enacted by governments, and when. During the pandemic this helped decision-makers and citizens understand governmental responses in a consistent way, aiding efforts to fight the pandemic. Now that covid-19 is no longer designated a public health emergency of international concern, the data can be used for research purposes and to prepare for future pandemics.
The OxCGRT systematically collected information on several different common policy responses governments took over 2020, 2021, and 2022, recorded these policies on a scale to reflect the extent of government action, and aggregates these scores into a suite of policy indices. We also collected differentiated policies data where different policies apply to people who were vaccinated and non-vaccinated.
The OxCGRT reports publicly available information on 24 policy indicators and a miscellaneous notes field of government response organised into four groups:
- C: containment and closure policies
- E: economic policies
- H: health system policies
- V: vaccination policies
To help make sense of the data, we have produced four indices that aggregate the data into a single number. For more details about how the indices are comprised, see the section 'Calculation of policy indices' in our documentation. Each of these indices reports a number between 0 to 100 that reflects the level of the government's response along certain dimensions:
- overall government response index (all indicators)
- containment and health index (all C and H indicators)
- stringency index (all C indicators, plus H1 which records public information campaigns)
- economic support index (all E indicators)
These indices are a measure of how many of the relevant policy types a government has acted upon, and to what degree. The index cannot say whether a government's policy has been implemented effectively.
Because of the complexity of the dataset, it is published across 27 CSV files. Our technical documentation ( https://github.com/OxCGRT/covid-policy-dataset/blob/main/documentation_and_codebook.md ) contains all the information users need to navigate and use the data.
How we process data at Our World in Data
All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.
At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.
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CBP’s MPC app is a free application that can be downloaded from the Apple App store and Google Play and is currently available for eligible travelers to use upon arrival at MPC approved sites . See FAQ question number 2 to see a complete list of current MPC Sites.
New MPC News
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Visit our Mobile Apps Directory for other approved CBP applications for easy download from the Google Play and the Apple App Store.
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After downloading the MPC Application from the Google Play Store or Apple App Store, eligible travelers will be prompted to create a profile with their passport or LPR card information. Upon arrival at eligible Preclearance locations users must select their mode of entry in the application as "Preclearance" before selecting their departing Preclearance Port of Entry. If a traveler is not using preclearance, upon landing in the United States, travelers will select "U.S. airport" as their mode of entry and then select their arrival airport or seaport and terminal. Travelers will then take a self-photo and answer a series of CBP inspection-related questions. Once the traveler submits their transaction through the app, travelers will then bring their physical passport to a CBP officer to finalize their inspection for entry into the United States.
Cellular or Wi-Fi connectivity is required to send information, but travelers may complete profile(s) without being connected to the internet.
Where can eligible travelers use MPC?
CBP uses the Mobile Passport Control (MPC) application to streamline the processing of eligible travelers entering the United States. Eligible travelers with a smartphone or tablet may voluntarily download the Mobile Passport Control (MPC)-enabled mobile application (app) from a mobile application store (e.g., Apple App Store or Google Play Store).
The MPC mobile app, is available to U.S. citizens, U.S. lawful permanent residents, Canadian B1/B2 citizen visitors and returning Visa Waiver Program travelers with approved ESTA. MPC is currently available at the following 52 sites, including 34 U.S. International Airports, 14 Preclearance locations, and 4 seaports of entry:
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- Shannon Airport (SNN)
- Tampa International Airport (TPA)
- Toronto Pearson International Airport (YYZ)
- Vancouver International Airport (YVR)
- Winnipeg James A Richardson International Airport (YWG)
What are the benefits of using MPC?
MPC provides a more efficient in-person inspection between the CBP officer and the traveler. Since the administrative tasks are performed by the traveler prior to the passport control inspection, MPC reduces passport control inspection time and overall wait times.
When will I be able to submit my information to CBP through an MPC app?
Travelers should submit their information upon arrival at the port of entry, including preclearance locations.
Does MPC replace my passport?
No, travelers must present a valid U.S. Passport, U.S. lawful permanent resident card, Canadian passport or a Visa Waiver Program passport to a CBP officer upon arrival.
Will I still have to go through the processing lines if I use MPC?
Yes, travelers will still be processed by a CBP officer. Travelers using MPC will be directed to a specific processing lane for a streamlined entry process.
Do travelers still have to fill out a declaration form?
No. Travelers will answer CBP inspection-related questions electronically via their smartphone or tablet. If a traveler has already filled out a CBP declaration form, it can be turned in to a CBP Officer.
Can groups use MPC?
Yes, up to 12 travelers can be added under 1 group submission.
Is using MPC secure?
Yes, the traveler’s document information and answers to CBP inspection-related questions are submitted directly to CBP via secure encryption protocols. The information you enter on the authorized applications are securely transmitted to CBP which sends a response to the apps, generating the electronic receipt you may display when entering the MPC queue. The authorized apps provide the option to store your profile on your smartphone or tablet for future travel or deleting it after your trip. Please note that the photo taken on the application will need to be updated periodically. CBP recommends travelers review the application’s privacy policy prior to download and use. Only information entered after the traveler acknowledges CBP’s notices (i.e., CBP’s mobile privacy policy, Section 311 of the Trade Facilitation and Trade Enforcement Act of 2015, Disclaimer Statement and the Paperwork Reduction Act Notice) is required to be compliant with CBP’s MPC business requirements.
What happens if my flight is diverted to a different airport?
Travelers will not be able to use MPC if their flight is diverted to a U.S. airport that does not utilize MPC processing. Upon arrival at the diverted airport, travelers will follow standard processing procedures. If you were processed by a CBP officer in Preclearance, you would land as a domestic passenger and can proceed to your destination or connecting flight.
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International travel‐related control measures to contain the COVID‐19 pandemic: a rapid review
In late 2019, the first cases of coronavirus disease 2019 (COVID‐19) were reported in Wuhan, China, followed by a worldwide spread. Numerous countries have implemented control measures related to international travel, including border closures, travel restrictions, screening at borders, and quarantine of travellers.
To assess the effectiveness of international travel‐related control measures during the COVID‐19 pandemic on infectious disease transmission and screening‐related outcomes.
Search methods
We searched MEDLINE, Embase and COVID‐19‐specific databases, including the Cochrane COVID‐19 Study Register and the WHO Global Database on COVID‐19 Research to 13 November 2020.
Selection criteria
We considered experimental, quasi‐experimental, observational and modelling studies assessing the effects of travel‐related control measures affecting human travel across international borders during the COVID‐19 pandemic. In the original review, we also considered evidence on severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). In this version we decided to focus on COVID‐19 evidence only. Primary outcome categories were (i) cases avoided, (ii) cases detected, and (iii) a shift in epidemic development. Secondary outcomes were other infectious disease transmission outcomes, healthcare utilisation, resource requirements and adverse effects if identified in studies assessing at least one primary outcome.
Data collection and analysis
Two review authors independently screened titles and abstracts and subsequently full texts. For studies included in the analysis, one review author extracted data and appraised the study. At least one additional review author checked for correctness of data. To assess the risk of bias and quality of included studies, we used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS‐2) tool for observational studies concerned with screening, and a bespoke tool for modelling studies. We synthesised findings narratively. One review author assessed the certainty of evidence with GRADE, and several review authors discussed these GRADE judgements.
Main results
Overall, we included 62 unique studies in the analysis; 49 were modelling studies and 13 were observational studies. Studies covered a variety of settings and levels of community transmission.
Most studies compared travel‐related control measures against a counterfactual scenario in which the measure was not implemented. However, some modelling studies described additional comparator scenarios, such as different levels of stringency of the measures (including relaxation of restrictions), or a combination of measures.
Concerns with the quality of modelling studies related to potentially inappropriate assumptions about the structure and input parameters, and an inadequate assessment of model uncertainty. Concerns with risk of bias in observational studies related to the selection of travellers and the reference test, and unclear reporting of certain methodological aspects.
Below we outline the results for each intervention category by illustrating the findings from selected outcomes.
Travel restrictions reducing or stopping cross‐border travel (31 modelling studies)
The studies assessed cases avoided and shift in epidemic development. We found very low‐certainty evidence for a reduction in COVID‐19 cases in the community (13 studies) and cases exported or imported (9 studies). Most studies reported positive effects, with effect sizes varying widely; only a few studies showed no effect.
There was very low‐certainty evidence that cross‐border travel controls can slow the spread of COVID‐19. Most studies predicted positive effects, however, results from individual studies varied from a delay of less than one day to a delay of 85 days; very few studies predicted no effect of the measure.
Screening at borders (13 modelling studies; 13 observational studies)
Screening measures covered symptom/exposure‐based screening or test‐based screening (commonly specifying polymerase chain reaction (PCR) testing), or both, before departure or upon or within a few days of arrival. Studies assessed cases avoided, shift in epidemic development and cases detected. Studies generally predicted or observed some benefit from screening at borders, however these varied widely.
For symptom/exposure‐based screening, one modelling study reported that global implementation of screening measures would reduce the number of cases exported per day from another country by 82% (95% confidence interval (CI) 72% to 95%) (moderate‐certainty evidence). Four modelling studies predicted delays in epidemic development, although there was wide variation in the results between the studies (very low‐certainty evidence). Four modelling studies predicted that the proportion of cases detected would range from 1% to 53% (very low‐certainty evidence). Nine observational studies observed the detected proportion to range from 0% to 100% (very low‐certainty evidence), although all but one study observed this proportion to be less than 54%.
For test‐based screening, one modelling study provided very low‐certainty evidence for the number of cases avoided. It reported that testing travellers reduced imported or exported cases as well as secondary cases. Five observational studies observed that the proportion of cases detected varied from 58% to 90% (very low‐certainty evidence).
Quarantine (12 modelling studies)
The studies assessed cases avoided, shift in epidemic development and cases detected. All studies suggested some benefit of quarantine, however the magnitude of the effect ranged from small to large across the different outcomes (very low‐ to low‐certainty evidence). Three modelling studies predicted that the reduction in the number of cases in the community ranged from 450 to over 64,000 fewer cases (very low‐certainty evidence). The variation in effect was possibly related to the duration of quarantine and compliance.
Quarantine and screening at borders (7 modelling studies; 4 observational studies)
The studies assessed shift in epidemic development and cases detected. Most studies predicted positive effects for the combined measures with varying magnitudes (very low‐ to low‐certainty evidence). Four observational studies observed that the proportion of cases detected for quarantine and screening at borders ranged from 68% to 92% (low‐certainty evidence). The variation may depend on how the measures were combined, including the length of the quarantine period and days when the test was conducted in quarantine.
Authors' conclusions
With much of the evidence derived from modelling studies, notably for travel restrictions reducing or stopping cross‐border travel and quarantine of travellers, there is a lack of 'real‐world' evidence. The certainty of the evidence for most travel‐related control measures and outcomes is very low and the true effects are likely to be substantially different from those reported here. Broadly, travel restrictions may limit the spread of disease across national borders. Symptom/exposure‐based screening measures at borders on their own are likely not effective; PCR testing at borders as a screening measure likely detects more cases than symptom/exposure‐based screening at borders, although if performed only upon arrival this will likely also miss a meaningful proportion of cases. Quarantine, based on a sufficiently long quarantine period and high compliance is likely to largely avoid further transmission from travellers. Combining quarantine with PCR testing at borders will likely improve effectiveness. Many studies suggest that effects depend on factors, such as levels of community transmission, travel volumes and duration, other public health measures in place, and the exact specification and timing of the measure. Future research should be better reported, employ a range of designs beyond modelling and assess potential benefits and harms of the travel‐related control measures from a societal perspective.
Plain language summary
Can international travel‐related control measures contain the spread of the COVID‐19 pandemic?
What are international travel‐related control measures?
International travel control measures are methods to manage international travel to contain the spread of COVID‐19. Measures include:
‐ closing international borders to stop travellers crossing from one country to another;
‐ restricting travel to and from certain countries, particularly those with high infection levels;
‐ screening or testing travellers entering or leaving a country if they have symptoms or have been in contact with an infected person;
‐ quarantining newly‐arrived travellers from another country, that is, requiring travellers to stay at home or in a specific place for a certain time.
What did we want to find out?
We wanted to find out how effective international travel‐related control measures are in containing the COVID‐19 pandemic.
What we did
We searched for studies on the effects of these measures on the spread of COVID‐19. Studies had to report how many cases these measures prevented or detected, or whether they changed the course of the pandemic. The studies could include people of any age, anywhere. They could be of any design including those that used ‘real‐life’ data (observational studies) or hypothetical data from computer‐generated simulations (modelling studies).
This is the first update of our review. This update includes only studies on COVID‐19, published up to 13 November 2020.
What we found
We found 62 studies. Most (49 studies) were modelling studies; only 13 used real‐life data (observational studies). Studies took place across the world and at different times during the pandemic. Levels of COVID‐19 within countries varied.
Most studies compared current travel‐related control measures with no travel‐related controls. However, some modelling studies also compared current measures against possible measures, for example, to see what might happen if controls were more or less relaxed or were combined with other measures.
Below we summarise the findings of some outcomes.
Most studies showed that travel restrictions reducing or stopping cross‐border travel were beneficial, but this beneficial effect ranged from small to large. Additionally, some studies found no effect. Studies also predicted that these restrictions would delay the outbreak, but the delay ranged from one day to 85 days in different studies.
Screening at borders (13 modelling studies and 13 observational studies)
These studies assessed screening at borders, including screening people with symptoms or who had potentially been exposed to COVID‐19, or testing people, before or after they travelled.
For screening based on symptoms or potential exposure to COVID‐19, modelling studies found that screening reduced imported or exported cases and delayed outbreaks. Modelling studies predicted that 1% to 53% of cases would be detected. Observational studies reported a wide range of cases detected, from 0% to 100%, with the majority of studies reporting less than 54% of cases detected.
For screening based on testing, studies reported that testing travellers reduced imported or exported cases, and cases detected. Observational studies reported that the proportion of cases detected varied from 58% to 90%. This variation might be due to the timing of testing.
All studies suggested that quarantine may be beneficial, but the size of this effect ranged from small to large in the different studies. Modelling studies, for example, predicted that quarantine could lead to between 450 and over 64,000 fewer cases in the community. Differences in effects may depend on how long people were quarantined for and how well they followed the rules.
Quarantine and screening at borders (7 modelling studies and 4 observational studies)
For quarantine and screening at borders, most studies suggested some benefit, however the size of this effect differed between studies. For example, observational studies reported that between 68% and 92% of cases would be detected. Differences in effects may depend on how long people were quarantined for and how often they were tested while in quarantine.
How reliable are these results?
Our confidence in these results is limited. Most studies were based on mathematical predictions (modelling), so we lack real‐life evidence. Further, we were not confident that models used correct assumptions, so our confidence in the evidence on travel restrictions and quarantine, in particular, is very low. Some studies were published quickly online as ‘preprints’. Preprints do not undergo the normal rigorous checks of published studies, so we are not certain how reliable they are. Also, the studies were very different from each other and their results varied according to the specification of each travel measure (e.g. the type of screening approach), how it was put into practice and enforced, the amount of cross‐border travel, levels of community transmission and other types of national measures to control the pandemic.
What this means
Overall, international travel‐related control measures may help to limit the spread of COVID‐19 across national borders. Restricting cross‐border travel can be a helpful measure. Screening travellers only for symptoms at borders is likely to miss many cases; testing may be more effective but may also miss cases if only performed upon arrival. Quarantine that lasts at least 10 days can prevent travellers spreading COVID‐19 and may be more effective if combined with another measure such as testing, especially if people follow the rules.
Future research needs to be better reported. More studies should focus on real‐life evidence, and should assess potential benefits and risks of travel‐related control measures to individuals and society as a whole.
Summary of findings
Summary of findings 1.
a Downgraded ‐1 for risk of bias, due to major quality concerns in some studies related to the appropriateness of the model’s structural elements, the input parameters, and the adequacy of assessment of the model’s uncertainty. b Downgraded ‐1 for imprecision, due to a wide range of plausible effects. c Downgraded ‐1 for indirectness, due to no reporting of external validation in some studies and/or concerns with reporting of external validation in others. d Downgraded ‐1 for risk of bias, due to major quality concerns in some studies related to the appropriateness of the model’s structural elements, the input parameters, the adequacy of assessment of the model’s uncertainty, and incomplete technical documentation. e Downgraded ‐1 for risk of bias, due to major quality concerns in some studies related to the appropriateness of the model’s structural elements and the adequacy of assessment of the model’s uncertainty. f Downgraded ‐1 for risk of bias, due to major quality concerns in some studies related to the appropriateness of the model’s structural elements, the adequacy of assessment of the model’s uncertainty and the lack of technical documentation. g Downgraded ‐1 for imprecision, due to effect estimates being unavailable. h Downgraded ‐1 for imprecision, due to only one contributing study. i Downgraded ‐1 for imprecision, due to insufficient data reported to enable assessment of precision. j Downgraded ‐1 for risk of bias, due to major quality concerns in some studies related to the appropriateness of the model's structural elements and input parameters. k Downgraded ‐1 for risk of bias, due to major quality concerns in some studies related to the appropriateness of the models's structural elements. l Downgraded ‐1 for indirectness, due to no reporting of external validation in all included studies. m Downgraded ‐1 for risk of bias, due to major quality concerns in some studies related to the adequacy of assessment of the model’s uncertainty.
Summary of findings 2
a Downgraded ‐1 for imprecision, due to only one contributing study.
b Downgraded ‐1 for risk of bias, due to major quality concerns in some studies related to the appropriateness of the model’s structural elements, the input parameters, and the adequacy of assessment of the model’s uncertainty.
c Downgraded ‐1 for imprecision, due to a wide range of plausible effects
d Downgraded ‐1 for indirectness, due to no reporting of external validation in some studies and concerns with reporting of external validation in others.
e Downgraded ‐1 for indirectness, due to no reporting of external validation in all included studies.
f Downgraded ‐1 for risk of bias, due to concerns with traveller selection, the reference test, and the flow and timing of procedures.
g Downgraded ‐1 for indirectness, as travellers on evacuation flights and cruise ships comprised most of the studies; these are likely not representative of usual travels.
h Downgraded ‐1 for risk of bias, due to major quality concerns in some studies related to the appropriateness of the model’s structural elements and the adequacy of assessment of the model’s uncertainty.
i Downgraded ‐1 for risk of bias, due to major quality concerns in some studies related to the adequacy of assessment of the model’s uncertainty.
Summary of findings 3
a Downgraded ‐1 for risk of bias, due to major quality concerns in some studies related to the adequacy of assessment of the model’s uncertainty and incomplete technical documentation.
b Downgraded ‐1 for imprecision, due to insufficient data reported to enable assessment of precision.
c Downgraded ‐1 for indirectness, due to no reporting of external validation in some studies and concerns with reporting of external validation in others.
d Downgraded ‐1 for risk of bias, due to major quality concerns in some studies related to the appropriateness of the models’ structural assumptions and adequacy of assessment of the model’s uncertainty.
e Downgraded ‐1 for imprecision, due to only one contributing study.
f Downgraded ‐1 for indirectness, due to no reporting of external validation in included studies.
g Downgraded ‐1 for risk of bias, due to major quality concerns in some studies related to the appropriateness of the models’ structural assumptions, the input parameters and the adequacy of assessment of the model’s uncertainty.
h Downgraded ‐1 for imprecision, due to a wide range of plausible effects.
I Downgraded ‐1 for risk of bias, due to major quality concerns in some studies related to the adequacy of assessment of the model’s uncertainty.
Summary of findings 4
a Downgraded ‐1 for risk of bias, due to major quality concerns in some studies related to the appropriateness of the model’s structural assumptions, the input parameters, and the adequacy of assessment of the model’s uncertainty.
b Downgraded ‐1 for imprecision, due to a wide range of plausible effects.
c Downgraded ‐1 for indirectness, due to no reporting of external validation in included studies.
d Downgraded ‐1 for risk of bias, due to major quality concerns in some studies related to the adequacy of assessment of the model’s uncertainty.
e Downgraded ‐1 for risk of bias, due to major quality concerns in some studies related to the appropriateness of the model’s structural assumptions and the adequacy of assessment of the model’s uncertainty.
f Downgraded ‐1 for indirectness, as travellers on evacuation flights comprised most of the studies; these are likely not representative of usual travels.
Description of the condition
The first case of the novel coronavirus disease 2019 (COVID‐19) was reported in Wuhan, Hubei, China in late 2019. Over the following weeks, the disease spread further in China and several other Asian countries, including Japan, South Korea, and Thailand ( WHO 2020a ). By mid‐March 2020, COVID‐19 cases had been reported in over 100 countries across the globe. On 11 March 2020, the World Health Organization (WHO) declared the outbreak to be a global pandemic ( WHO 2020b ).
COVID‐19 is caused by SARS‐CoV‐2, a virus closely related to those of the coronaviruses which cause severe acute respiratory syndrome (SARS‐CoV‐1/SARS) and Middle East respiratory syndrome (MERS‐CoV/MERS). However, in comparison with these viruses, SARS‐CoV‐2 has higher transmissibility and lower pathogenicity ( Fani 2020 ). Most people infected with SARS‐CoV‐2 have mild disease with non‐specific symptoms ( Wu 2020 ). The proportion of cases becoming critically ill, with respiratory failure, septic shock, multiple organ failure, or a combination of two or all of these, has been reported as 5% in China ( Wu 2020 ). The length of stay in hospital varies from less than one week to nearly two months; the length of stay in intensive care ranges from one to three weeks ( Rees 2020 ). Among hospitalised patients, mortality from COVID‐19 is reported to be 20% (95% confidence interval (CI) 18% to 23%), 23% (95% CI 19% to 27%) and 11% (95% CI 7% to 16%) in the USA, Europe and China, respectively ( Dorjee 2020 ). Although long‐term research is still lacking, there is also growing concern over 'long COVID', defined as “signs and symptoms that develop during or following an infection consistent with COVID‐19 and which continue for more than four weeks and are not explained by an alternative diagnosis” ( NICE 2020 ). Long COVID is likely to affect 10% or more of those who have tested positive for SARS‐CoV‐2 ( Carfi 2020 ; Greenhalgh 2020 ). Even a mild course of COVID‐19 may be associated with long‐term symptoms, most commonly cough, fever and fatigue, but also shortness of breath and chest pain, headaches and neurocognitive difficulties, and various mental health conditions ( Greenhalgh 2020 ). It is estimated that between 4% and 41% of infected individuals never develop symptoms ( Byambasuren 2020 ). Both presymptomatic and asymptomatic transmission have been described and are likely to play an important role in the dynamics of the pandemic ( Furukawa 2020 ).
A range of non‐pharmacological interventions (NPIs) have been put into place by governments to contain and mitigate the spread of COVID‐19. Given the lack of a drug to prevent SARS‐CoV‐2 infection, the current stage of vaccine distribution and provision, and the limited pharmacological interventions to treat COVID‐19, NPIs will continue to play a critical role in containing the SARS‐CoV‐2 pandemic for a significant period of time to come. Travel‐related control measures, one important type of NPI, range from the screening of travellers entering or leaving a country to the complete closure of national borders. Starting from February 2020, many countries and regions in the world implemented some type of travel‐related control measure, and these continue to be implemented across many countries. As the pandemic continues across the globe, with many countries having experienced a second wave of infection, and others having moved beyond this second wave, it is crucial to understand the effectiveness of these measures, including at what point in an outbreak they should be implemented and when they can be relaxed. Such knowledge will help to inform decisions on implementation or re‐implementation, relaxation or suspension of these measures, as well as potential modifications to them, and will help to guide public health resource allocation. This is in line with the WHO's International Health Regulations 2005, which call to ground public health decision‐making in scientific evidence ( WHO 2005 ).
Description of the intervention
Travel‐related control measures comprise different interventions, including the complete closure of national borders to entry or exit, or both; travel restrictions reducing or stopping cross‐border travel (e.g. denial of entry or exit on the basis of nationality, travel history, health status or other characteristics, suspension of travel via air, land, and sea); symptom/exposure‐based screening at borders; test‐based screening at borders; and quarantine of travellers. These measures can be implemented for all modes of travel, including air, land, and sea.
Travel‐related public health measures have a long tradition as a means of preventing the spread of epidemic diseases. Historic examples include the prevention of the spread of bubonic plague through widespread travel‐related quarantine in medieval port towns and other locations ( Tognotti 2013 ). More recently, entry screening at national borders was implemented during the SARS epidemic in 2003, and airport exit screening measures were used in efforts to contain the Ebola epidemic in West Africa and the Democratic Republic of Congo between 2014 and 2016 ( Mouchtouri 2019 ).
In 2019, the WHO developed guidelines on non‐pharmacological public health measures for mitigating the risk and impact of epidemic and pandemic influenza. Based on a systematic review of the evidence, internal (i.e. subnational) travel restrictions were among the measures recommended during early stages of extraordinary, localised influenza epidemics. In contrast, entry and exit screening were not recommended due to overall ineffectiveness of the measure, and border closures were not recommended, unless required by national law or in extraordinary circumstances ( WHO 2019 ). However, the transmission characteristics of influenza are different from those of SARS‐CoV‐2 and these insights are therefore not directly applicable to SARS‐CoV‐2. More directly relevant, two reviews assessed the effectiveness of travel‐related control measures in the context of the SARS‐CoV‐1, MERS‐CoV and other infectious disease epidemics ( Errett 2020 ; Mouchtouri 2019 ). One review reports that effectiveness was limited, as few infected travellers were identified; however, the review finds secondary potential benefits, such as raising awareness and discouraging sick individuals from travelling ( Mouchtouri 2019 ). The second review examined the impact of travel reductions on the spread of infectious diseases other than influenza, and concluded that these had some success in reducing disease spread across countries, but did not halt transmission. It also emphasised the potentially high social, economic, and political costs of travel bans ( Errett 2020 ). Undertaken in the context of the ongoing SARS‐CoV‐2 pandemic, a Cochrane Rapid Review examined, among other quarantine measures, the effectiveness of quarantining individuals travelling from countries with a declared outbreak ( Nussbaumer‐Streit 2020 ). This review found very low‐certainty evidence for a small effect for SARS and a potentially larger effect for COVID‐19 ( Nussbaumer‐Streit 2020 ). Thus, the evidence regarding the effectiveness of travel‐related control measures to prevent infectious disease spread is mixed and incomplete. Importantly, given the different transmission characteristics of influenza and the likely high rate of asymptomatic transmission for SARS‐CoV‐2 as compared to SARS‐CoV‐1 or MERS‐CoV, many of the insights gained from these other pathogens are not directly transferable. Consequently, a systematic review of the effectiveness of travel‐related control measures drawing on the growing evidence base from the COVID‐19 pandemic is warranted.
How the intervention might work
Travel‐related control measures limit the mobility of potential human carriers of infection when crossing national (and in principle, subnational) borders. These restrictions can be imposed on travellers arriving or leaving via land, air, or sea and are usually implemented by government agencies. The main idea behind all of these measures is to prevent the introduction of an infectious agent (in the present context, SARS‐CoV‐2) into a country, to reduce or delay the spread of an infectious disease within a country, or both. The intervention thus seeks to achieve a shift in epidemic development, whether by avoiding the epidemic entirely (i.e. cases do not occur at all), by reducing the peak of the epidemic (i.e. fewer cases occur, or are spread over a longer time period) or by delaying the arrival or peak of the epidemic (i.e. cases occur later).
All travel‐related control measures are based on the notion that travellers (all travellers or those from specific regions or with specific characteristics) represent a population at risk of being infected and of spreading the infection. For SARS‐CoV‐2, the risk of an infected person travelling and being unaware of being infected is compounded by the fact that presymptomatic and asymptomatic transmission are likely to play an important role. The intervention works by:
- stopping travel (i.e. complete border closure);
- limiting the number of at‐risk individuals entering or exiting a country (i.e. travel restrictions);
- detecting infected individuals based on symptoms or testing for the virus (i.e. symptom/exposure‐based screening; test‐based screening); and
- preventing disease transmission until a person has been clearly identified as non‐infectious (i.e. quarantine).
In light of the high rates of pre‐ and asymptomatic transmission, certain travel‐related control measures may be more appropriate in the SARS‐CoV‐2 pandemic than others. For example, quarantine of travellers may prove more effective than entry and exit screening.
In addition to their intended positive impact on infectious disease dynamics, travel‐related control measures may also have negative health impacts, notably the well‐known side effects of quarantine and isolation on mental health. Moreover, they have far‐reaching economic, social, legal, ethical, and political implications ( Folayan 2015 ; Nuttal 2014 ; Nuzzo 2014 ).
To assess the effectiveness of international travel‐related control measures during the COVID‐19 pandemic on infectious disease transmission and screening‐related outcomes.
In May 2020, the WHO asked the review authors to develop an evidence map that would chart the evidence of various travel‐related control measures relevant to containing the COVID‐19 pandemic ( Movsisyan 2021 ). This map informed the scope and methodological considerations of a subsequent rapid review requested by the WHO. We first published this rapid review in September 2020 ( Burns 2020 ). Because the body of evidence on COVID‐19 is growing very quickly, the WHO requested the present (first) update of that review. The methods for the original rapid review were prespecified in a protocol that was submitted to and reviewed by Cochrane (see Appendix 1 ). The eligibility criteria were reviewed and agreed upon with WHO. The methods used in this update were largely identical to those employed in the original review; we transparently report below any instances where we have adapted the methods.
To conduct this rapid review, we employed abridged procedures of systematic reviewing at certain stages, according to the Cochrane guidance for rapid reviews ( Garritty 2020 ). Specifically, only one review author conducted data extraction, assessed the risk of bias in epidemiological studies and assessed the quality of modelling studies. One review author checked risk of bias and quality ratings of all studies for consistency and plausibility. At least one additional review author checked for the correctness of all data reported in the data synthesis. Two or more review authors discussed any uncertainties during these stages. To ensure that the abridged procedures did not compromise the methodological rigour of the review, but also to ensure that all stages of the review were conducted consistently and correctly, we assigned these data extraction, risk of bias and quality assessment tasks to experienced Cochrane review authors, and involved researchers with modelling expertise to assist with the data extraction and quality assessment of modelling studies. Furthermore, we piloted the procedures for each stage, conducted regular team meetings, and kept a list of rolling questions that were updated continuously.
Criteria for considering studies for this review
Study designs.
In the context of a global pandemic, evidence to inform decisions must be generated rapidly, meaning that methods traditionally used to evaluate the impact of interventions, such as randomised controlled trials (RCTs) or quasi‐experimental studies, while possible, may not be considered feasible, appropriate, timely or ethical. Indeed, in this specific context, simulation models developed to make predictions about the (highly uncertain) future often represent the only available evidence to guide decision‐making. To ensure that we captured all relevant study types, we considered a broad range of empirical studies of any size that provided a quantitative measure of impact, including experimental and quasi‐experimental studies, observational studies, and mathematical modelling studies. Thus, we included the following types of studies:
- Interrupted time series (ITS) studies
- Controlled before‐after (CBA) studies and difference‐in‐differences (DiD) studies
- Instrumental variable (IV) studies
- Regression discontinuity (RD) studies
- Cohort studies
- Case‐control studies
- Compartmental models (e.g. SEIR‐type models comprising multiple compartments, such as S: susceptible, E: exposed, I: infectious, R: recovered)
- Bayesian hierarchical models (i.e. models comprising several submodels to integrate observed data as well as uncertainty)
- Spatial models (i.e. modelling disease transmission spatially)
- Time‐series models (i.e. models that model the temporal nature of disease transmission using time‐series techniques)
To avoid the inappropriate exclusion of studies, we considered all studies providing a quantitative measure of impact, regardless of whether they were indicated by any of these labels. We considered studies published in peer‐reviewed journals as well as those published on preprint servers. Our rationale for including preprint articles was that in the context of a global pandemic, there may be a scientific as well as moral case for publishing studies at the earliest opportunity to inform the emergency response. We included any studies that had been registered but not yet published (in a peer‐reviewed journal or on a preprint server) as 'ongoing' studies.
We excluded the following types of studies and publications:
- Case reports
- Studies that did not provide a quantitative measure of impact (e.g. studies providing a graphical summary of the number of cases over time in relation to the introduction of control measures, qualitative studies)
- Diagnostic studies (e.g. assessing the sensitivity and specificity of different screening tests in general; we did, however, include studies on the use of screening tests at national borders as a travel‐related control measure)
- Non‐empirical studies (e.g. commentaries, editorials, non‐systematic literature reviews not reporting primary empirical data)
- Systematic reviews (although relevant reviews were used for backward citation searches)
- Conference abstracts
We included studies on human populations (without any age restriction) susceptible to SARS‐CoV‐2/COVID‐19. To be eligible, modelling studies had to use modelling parameters for disease transmission specified to reflect SARS‐CoV‐2/COVID‐19. In the original review, we also included studies on SARS‐CoV‐1/SARS and MERS‐CoV/MERS ( Burns 2020 ).
For this update, we excluded studies:
- not targeting human transmission;
- concerned with humans at risk of developing other infectious diseases, characterised by different transmission properties (e.g. SARS‐CoV‐1/SARS and MERS‐CoV/MERS, Ebola and viral meningitis, the transmission modes of which are primarily person‐to‐person, rather than airborne); and
- addressing humans at risk of developing other infectious diseases, for which travel‐related control measures do not play a significant role in containing outbreaks (e.g. influenza).
Interventions
We considered travel‐related control measures affecting human travel across national borders. We considered both introduction and implementation, as well as relaxation and de‐implementation of the following measures.
- Closure of national borders to entry or exit, or both, which stop cross‐border travel
- Denial of entry or exit, or both, on the basis of nationality, travel history, health status or other characteristics
- Full or partial suspension of cross‐border travel via any or all of land, air and sea
- Visa requirement or refusal on the basis of nationality, travel history, health status or other characteristics
- Temperature measurement (e.g. thermography)
- Health questionnaire (e.g. symptoms, travel history, contact history)
- Physical examination
- Testing for current or past infection
- Quarantine or isolation of travellers crossing national borders, including voluntary or government‐mandated quarantine of travellers for different durations and without any follow‐up measures, such as testing at certain days of the quarantine
- Any combination of the above measures
We excluded the following types of interventions.
- Combinations of the above‐mentioned travel‐related control measures with other measures where studies do not provide effect estimates for the travel‐related control measures (e.g. studies providing a combined effect estimate for suspension of cross‐border travel and use of mandatory face masks in the general population) Studies in which the effect of travel‐related control measures cannot be disentangled from the effect of a broader suite of public health measures cannot usefully inform WHO recommendations on whether countries should or should not consider travel‐related control measures to contain the COVID‐19 pandemic.
- All interventions not directly related to travel, including a range of containment and mitigation measures (e.g. community‐based quarantine, personal protective measures, hygiene measures, bans on mass gatherings and other social‐distancing measures).
- All interventions related to movement of animals or goods.
- All interventions concerned with human travel across subnational borders. While subnational measures can potentially inform national travel‐related control measures, these measures are not prioritised by the WHO. As shown in the previous evidence map ( Movsisyan 2021 ), they are also often impossible to disentangle from other subnational measures, such as lockdowns, community quarantine or social distancing recommendations.
- Travel warnings or travel advice issued by the WHO or national governments.
- Studies of interventions solely concerned with the accuracy of tests rather than their implementation as part of an entry and/or exit border control measure.
- Studies of interventions related to international travel but not concerned with cross‐border impacts, i.e. interventions to contain transmission within closed populations that only assessed their effect on these closed populations (e.g. on cruise ships, within detention centres). This exclusion criterion was added post hoc.
- Usual practice (e.g. seasonal changes to travel) or events (e.g. school holidays) affecting travel but not representing travel‐related control measures.
- Cancellation of events affecting international travel but undertaken as a means to prevent mass gatherings (e.g. Hajj, international sporting events, international trade fairs).
We included studies that assessed travel‐related control measures as specified above, targeting populations within one country (e.g. the lockdown of Wuhan, China) if their impact was assessed on the population of other countries (e.g. Australia). We additionally considered relevant restrictions between mainland China and Hong Kong and Taiwan, given the existence of a hard border and the implementation of travel‐related control measures analogous to those implemented internationally.
Other considerations
There are two Cochrane Rapid Reviews with overlapping studies. One published review focuses on quarantine measures, including quarantining travellers crossing national borders ( Nussbaumer‐Streit 2020 ). The other review is concerned with screening measures, including entry and exit screening at national borders ( Viswanathan 2020 ). In discussions with Cochrane and the WHO, we decided that it would be important for decision‐makers to be able to access the evidence on all travel‐related control measures in a single review. To address the overlap between the present review and the two separately conducted reviews, we checked our review findings with the findings from those reviews. While we identified a few overlapping studies, these are presented and discussed as part of different bodies of evidence and in relation to different scopes. We did not identify any discrepancies in reporting and interpretation. Along with our previous evidence map on travel‐related control measures ( Burns 2020 ), we considered these reviews for backward citation searches.
Comparator(s)
We included a range of possible comparators, such as a counterfactual scenario in which the intervention was not implemented, a complete relaxation of the measure, or a partial relaxation of the measure. Likewise, a scenario of no intervention could have been compared against a counterfactual scenario in which an intervention was implemented or relaxed. A relevant study therefore may compare an observed intervention with a simulated scenario of no intervention, while another study may compare simulated stringent interventions with simulated lax interventions, while yet another study may compare an observed intervention with a simulated intervention implemented at an earlier time.
Primary outcomes
We considered studies assessing any of the following infectious disease transmission and screening‐related outcomes.
- Cases avoided due to the measure (e.g. number, proportion, rate of cases observed or predicted in the community with and without the intervention).
- Shift in epidemic development due to the intervention (e.g. probability of epidemic, time to/delay in epidemic arrival or peak, size of epidemic peak, change in the effective reproduction number).
- Cases detected due to the measure: we focused on outcomes we felt are most relevant for decision‐makers in the current pandemic: the proportion of cases detected among the total number of cases (i.e. sensitivity, case detection rate) and the proportion of cases among those screening positive (i.e. the positive predictive value).
Secondary outcomes
We considered the following secondary outcomes if identified in studies that assessed at least one of the primary outcomes.
- Any other infectious disease transmission outcome (e.g. number of severe cases in the community)
- Healthcare utilisation (e.g. number of cases requiring treatment in the intensive care unit (ICU), time until ICU capacity is reached)
- Resource requirements for implementing the intervention (e.g. costs associated with intervention, additional personnel, number of tests required)
- Any adverse effects (e.g. health, economic and social outcomes)
- User acceptability (e.g. passenger confidence)
We did not assess user acceptability in the original review; following exchanges with the WHO, we added this secondary outcome to the update.
Search methods for identification of studies
The search strategy was structured around two blocks focusing firstly on COVID‐19, SARS and MERS, and secondly on travel‐related control measures. For the first block, we added search terms related to ‘test’ to make the strategy more sensitive to capturing studies on testing in this update. We conducted the searches in English but aimed to include studies published in any language. The search strategy was informed by the search strategy used in the evidence map for travel‐related control measures ( Movsisyan 2021 ). An experienced information specialist adapted and ran the searches, which were verified by a content expert and reviewed by Cochrane.
Electronic databases
For this update, we ran searches in the following electronic databases.
- Ovid MEDLINE and Epub Ahead of Print, In‐Process & Other Non‐Indexed Citations, Daily and Versions (1946 to 13 November 2020)
- Ovid Embase (1996 to 13 November 2020)
Other searches
We additionally searched the following COVID‐19‐specific databases.
- Cochrane COVID‐19 Study Register (covid-19.cochrane.org), which contains study references from ClinicalTrials.gov, WHO International Clinical Trials Registry Platform (ICTRP), PubMed, medRxiv and other handsearched articles from publishers’ websites.
- WHO 'Global literature on coronavirus disease' database (search.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov), which primarily contains research (published and/or prepublication) articles indexed in PubMed, Web of Science, Global Index Medicus and Embase. In addition, Lanzhou University (Lanzhou, China) submits citations on a daily basis from the China National Knowledge Infrastructure (CNKI) as well as a number of Chinese journal publishers. Due to high overlap across our sources, we added a filter here to exclude records from MEDLINE and Embase.
In the original review, we also searched the US Center for Disease Control and Prevention (CDC) COVID‐19 Research Articles Downloadable Database, but this resource is no longer available. Instead, the contents of this database are now contained in both the Cochrane COVID‐19 Study Register and the WHO 'Global literature on coronavirus disease' database.
Finally, we conducted backward citation searches of systematic reviews on travel‐related control measures known to us or identified through our searches (see Appendix 2 ) to identify additional eligible studies. The full search strategy is presented in Appendix 3 .
Selection of studies
To harmonise the screening process, we asked all review authors involved with the title and abstract screening to screen an initial set of the same 50 studies, after which we organised a group call to discuss any issues. In the original review, one review author screened all titles and abstracts, while a second review author screened only those excluded by the first review author. For this update, we screened all titles and abstracts in duplicate. The team conducted title and abstract screening using the Rayyan online systematic review software ( Ouzzani 2016 ).
As with the title and abstract screening process, we harmonised the full‐text screening process by asking all review authors involved with full‐text screening to screen an initial same set of 10 studies ( Garritty 2020 ). The team then discussed any open questions or issues in a group call. Subsequently, two review authors working independently each screened the remaining full‐text records in duplicate. The two review authors discussed any discrepancies, and consulted a third review author or the entire author team where necessary until they reached consensus. We recorded reasons for exclusion for all studies excluded at the full‐text screening stage.
Inclusion of non‐English language studies
We considered studies published in all languages. Within the review team, we were able to consider studies in Armenian, English, French, German, Italian, Russian and Spanish, and sought help with translation for any other languages, where needed. We screened a small number of studies in other languages at the title and abstract screening stage, including some with an English abstract and some written in German and Spanish, however, we did not identify any relevant studies in any languages other than English.
Excluding eligible studies from the analysis
For this update, we made the post hoc decision to exclude from the analysis several studies meeting the review eligibility criteria. During data extraction and synthesis, we found these studies to be less informative or potentially misleading for decision‐making. These studies included: (1) observational screening studies with limited data; (2) observational ecological studies; and (3) modelling studies using overly simplistic or theoretical assumptions and presenting abstract findings.
- Some observational studies evaluating entry and/or exit screening measures reported only limited data regarding the effectiveness of the measure. These studies report, for example, how many individuals have been screened, how many were screened positively, and how many were COVID‐19 cases. However, due to the lack of a reference test, the true number of cases is unknown. As a result, these studies provide information on how many cases were detected, but not on how many cases were missed; thus we feel that these studies are not sufficiently informative for decision‐makers. We also excluded such studies from the analysis in the original review.
- Observational ecological studies examine the aggregated impact of various travel‐related control measures across countries, and, in principle, such studies could be of interest to decision‐makers. However, the aggregated nature of the data places these ecological studies at even higher risk of bias than other observational studies, making them even less able to deliver causal insights. Moreover, interventions and outcomes, and the associated results, tend to be operationalised in a simplified manner across countries. Consequently, we felt that these studies were at high risk of delivering over‐simplified and biased results.
- All modelling studies providing an assessment of the impact of travel‐related control measures make some assumptions to simulate the real‐world; these assumptions relate to aspects such as the intervention itself, the travel scenario and/or the regions implementing and being restricted by the intervention. Studies in which most of these aspects use simplistic or conceptual assumptions, however, tend to provide abstract findings that cannot readily be interpreted or applied. We feel that mainly theoretical studies are not sufficiently informative for decision‐makers.
Data extraction and management
One review author extracted study characteristics and data from all included main studies using a data extraction form in Microsoft Excel. All extracted data were checked by a second review author. We piloted the data extraction form, using three studies that represented different intervention types and that met the inclusion criteria. Appendix 4 provides the details on the data extraction categories. For studies excluded from the analysis, we extracted descriptive characteristics relating to the PICO elements, as well as a short narrative description of the results. To do so, we used a simplified version of the data extraction form used for the main studies.
In the review protocol (see Appendix 1 ), we specified that we would consider searching for data from external sources to enhance our understanding of the design features of the travel‐related control measures and the stage of the pandemic at the time these were implemented. However, given the lack of comprehensive reporting and the inconsistency of the information provided across these sources (e.g. discrepancies in how WHO reports described the stage of the pandemic in earlier months), and given that this information was largely not applicable to modelling studies, we decided against using these sources.
Assessment of risk of bias in included studies
One review author rated the risk of bias or the quality of each included study, depending on the type of study, and a second review author checked the judgements. The studies excluded from the review analysis were not further assessed at this stage. The team of review authors involved with assessing risk of bias and quality was largely the same for this update as for the original review. Given that one new review author was involved with this step, at the outset we discussed how to correctly and consistently apply each of the tools to one screening study and two modelling studies before beginning the assessment. These review authors discussed any questions or uncertainties that arose during the process.
Given the broad range of study designs, we applied multiple tools in assessing the risk of bias or quality of included studies, with the same tools applied in the original review and the present update. We had planned to use version 2 of the Cochrane 'Risk of bias' tool for experimental studies ( Higgins 2019 ), and ROBINS‐I for quasi‐experimental and observational intervention studies ( Sterne 2016 ). However, we did not identify any experimental studies. We identified two synthetic control studies, which are generally considered a type of quasi‐experimental study. However, given that ROBINS‐I was not developed for this type of quasi‐experimental study with more sophisticated statistical methods, we assessed these studies with the quality appraisal tool developed for modelling studies, as described below.
To appropriately assess the risk of bias of observational studies evaluating screening at borders, which are more closely related to diagnostic studies than intervention evaluations, we decided post‐protocol to apply the Quality Assessment of Diagnostic Accuracy Studies (QUADAS‐2) tool ( Whiting 2011 ), as also employed in the Cochrane review on screening measures to control COVID‐19 ( Viswanathan 2020 ). This tool comprises four domains: participant selection (i.e. passenger/traveller selection, for our purposes), the index test, the reference standard, and the flow and timing. For each of these domains, using a series of signalling questions, we provided a judgment of ‘low’, ‘unclear’ or ‘high’ risk of bias for each study. Additionally, the tool facilitates a concrete assessment of generalisability through considering how the population, index test and reference standard compares with the aspects of interest in this review. In line with QUADAS‐2 guidance, we considered how best to apply the tool to our specific review question. The tool, including the specifications we applied in making judgements, is outlined in Appendix 5 .
As described in the original review ( Burns 2020 ), no validated tool is available for assessing the risk of bias of modelling studies. Following the suggestions by ( Egger 2017 ), we developed a bespoke tool for the assessment of modelling studies and selected criteria from a rapid review of the methodological literature ( Philips 2006 ) and two methodological studies ( Caro 2014 ; Egger 2017 ). The tool comprises the following domains: (i) model structure, (ii) input data, (iii) validation, (iv) uncertainty and (v) transparency. The individual criteria we applied, in the form of signalling questions, are outlined in Appendix 6 . We reported each of the criteria separately, that is, we did not combine multiple criteria into a summary score. This also allows for a distinction between ‘fatal flaw indicators’, notably inappropriate structural assumptions and input parameters, and other aspects of model quality and credibility, such as internal and external model validation ( Caro 2014 ).
Contacting study authors
We contacted study authors to request additional information where unclear or non‐reported aspects precluded the assessment of eligibility or inclusion in the data synthesis.
Data synthesis
Given that observational studies provide a measured estimate of effect whereas modelling studies predict such an effect, we treated these as two separate bodies of evidence in the synthesis (see also 'Assessment of certainty of evidence').
Due to substantial heterogeneity across included studies with regard to the setting, population, intervention and other contextual factors, as well as study methods, and as specified in the protocol ( Appendix 1 ), we decided that data were not sufficiently similar to conduct meta‐analyses. We therefore synthesised the findings narratively and in tabular form, stratified by intervention type and outcome. We adhered to the 'Synthesis without meta‐analysis' (SWiM) in systematic reviews reporting guideline ( Campbell 2020 ).
Part one of the narrative synthesis comprised four steps in moving from the effects reported at the individual study‐level to a summary across studies: (i) we created a study‐by‐study table describing the effects of interventions, as well as potential effect moderators, as estimated in each included study; (ii) we classified the effect direction for each reported intervention effect, following recent guidance ( Hilton Boon 2020 ); (iii) for each intervention category and primary outcome, we subsequently looked across contributing studies to develop the summary of findings, including a description of the proportion of studies predicting a positive, negative or no effect for the intervention; (iv) we abstracted this summary of findings for each intervention category and primary outcome into a concise narrative summary to present, along with the certainty of the evidence, in the 'Summary of findings' table and the 'Results' section of the review, paying particular attention to sources of heterogeneity (see below).
Part two involved determining the direction of effect for each intervention‐outcome pair, which could be a positive effect, no effect, mixed effect, or negative effect. For systematic reviews of public health interventions, a beneficial effect of any size beyond the null is often considered to be potentially relevant. Additionally, for travel‐related control measures, this minimal important difference is highly context‐dependent. For example, the role of international travel in importing cases, and the associated role of travel‐related control measures in containing the pandemic, will be different in countries where community transmission is not occurring compared to countries where community transmission is widespread. Consistent with this perspective, we did not consider the size of the effect in determining effect direction.
Specifically, we first specified the comparators used in each study (e.g. measure versus no measure or combined measure versus single measure). In determining effect direction, we classified an effect for which a better outcome was observed for the intervention condition than the comparator condition as ‘positive’, and an effect for which a worse outcome was observed for the intervention condition as ‘negative’. Only studies in which the two conditions reported identical effect estimates were classified as ‘no effect’. Many studies assessed an intervention in multiple countries or examined a range of scenarios related to a specific intervention (e.g. in the context of high‐, moderate‐ and low‐community transmission). Where studies observed consistent effect directions across these conditions, we classified the effect direction as such; where inconsistent effect directions were observed, we classified the effect direction as ‘mixed’.
Assessment of heterogeneity and subgroup analyses
In the absence of meta‐analyses, and given the substantial heterogeneity of included studies, we did not conduct analyses of subgroups. As part of our narrative synthesis, however, we aimed to identify potential sources of heterogeneity that may have influenced intervention effectiveness. Given methodological differences, as well as differences in interventions, contexts and outcomes, for modelling studies we focused on potential moderating factors (e.g. level of community transmission, stringency of intervention, level of travel after relaxation of intervention) that were assessed within a given study. The methods used to assess these potential moderators differed widely across individual studies, however we only considered data that were assessed and clearly reported as part of a formal analysis. Given that observational studies of entry and exit screening measures were relatively homogeneous, for these studies we examined potential moderators across studies (e.g. type of screening, timing of polymerase chain reaction (PCR) testing).
Assessment of certainty of evidence
We used the GRADE approach to assess the certainty of the primary outcomes. One review author collated the evidence for each primary outcome and suggested initial certainty of evidence ratings. These were then further deliberated in a team of review authors and a joint decision for certainty of evidence ratings was made for each primary outcome.
The certainty of evidence is defined in GRADE as the extent to which one can be confident that the true effect of an intervention lies on one side of a specified threshold, or within a chosen range ( Hultcrantz 2017 ). In the original review, as well as in this update, we considered 'difference from the null' as an important threshold, assuming that even small effect sizes may be relevant for population‐level travel‐related control measures, as noted above.
The certainty of evidence rating in GRADE yields four possible levels of evidence: high certainty (i.e. the estimated effect lies close to the true effect), moderate certainty (i.e. the estimated effect is probably close to the true effect), low certainty (i.e. the estimated effect might substantially differ from the true effect), and very low certainty (i.e. the estimated effect is probably substantially different from the true effect).
In accordance with our approach to data synthesis, we rated bodies of evidence from observational and modelling studies separately. In GRADE, evidence from RCTs enters the rating as high certainty, as does evidence from observational studies whose risk of bias has been assessed using ROBINS‐I ( Schünemann 2019 ). Subsequently, five domains are used to downgrade evidence, including study limitations, inconsistency, indirectness, imprecision and publication bias, and three domains are used to upgrade evidence, including plausible confounding, large estimates of effect, and dose‐response relationship. The upgrading applies only when evidence has not been downgraded.
To rate the certainty of evidence from modelling studies, we used the recent guidance developed by the GRADE Working Group ( Brozek 2021 ). As per the guidance, we initially assessed the body of evidence from modelling studies as high certainty and then used the domains described above to assess certainty of model outputs. We then applied the above domains to further downgrade or upgrade certainty of evidence from modelling studies, using tailored interpretations, as specified in the guidance. For example, risk of bias in modelling studies refers to the credibility of the model and its inputs; inconsistency assesses the difference in the results of two or more models; imprecision examines the model point estimate (e.g. predicted event) and the variability of that estimate; indirectness examines model outputs in relation to the prespecified PICO elements of interest; finally, publication bias refers to the likelihood that relevant models have been developed but not made available ( Brozek 2021 ).
To rate the certainty of evidence from observational studies assessing screening at borders in detecting cases (i.e. the proportion of cases detected and the positive predictive value), we used the GRADE guidance for rating the certainty of evidence for diagnostic tests and strategies ( Schünemann 2008 ). In accordance with the guidance, we initially rated the body of evidence from these cross‐sectional studies reporting an appropriate reference standard (e.g. a symptom/exposure‐based screening followed by PCR testing) as high‐certainty evidence. We then applied the five GRADE domains as described above to further downgrade evidence when deemed appropriate.
Results of the search
The PRISMA flow diagram ( Moher 2009 ), shown in Figure 1 describes the study selection process. For this update, we screened 3370 new unique records at the title and abstract screening stage (3033 identified through database searches and 337 through backward citation searches of systematic reviews), in addition to the 3036 unique records screened in the first version (6406 records in total). We screened the full texts of 243 new records, in addition to the 385 records that were screened in the first version (628 records in total). Overall, 88 records met the eligibility criteria for this update (comprising 60 new records in addition to the 28 records focusing on SARS‐CoV‐2/COVID‐19 included in the original review). Reasons for excluding studies at the full‐text screening stage are presented in Figure 1 . Ninety‐three of these studies, the exclusion of which was decided in rounds of discussion among the review authors, are further described in the Characteristics of excluded studies tables.
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Systematic review PRISMA flow diagram
Out of the 88 records, we excluded 22 records from the analysis; thus, these did not contribute effects to the data synthesis or inform conclusions. As described in more detail under 'Methods', these comprised observational screening studies with limited data ( Chang 2020 ; Expert‐Taskforce 2020 ; Gupta 2020 ; Hayakawa 2020 ; Ing 2020 ; Jernigan 2020 ; Potdar 2020 ; Sriwijitalai 2020a ), observational ecological studies ( Arshed 2020 ; Chaudhry 2020 ; Jablonska 2020 ; Koh 2020 ; Leffler 2020 ; Liu 2020a ; Ogundokun 2020 ; Stokes 2020 ; Teixeira da Silva 2020 ), and modelling studies for which a ‘real‐world’ effect cannot readily be interpreted ( Baba 2020 ; Chen 2020d ; Cacciapaglia 2020a ; Cacciapaglia 2020b ; Jorritsma 2020). The characteristics of these 22 studies are described in Appendix 7 .
We included 66 records in the analysis. These represent 62 unique studies, as four records assessed interventions already addressed by other included records ( Arima 2020 ; Bays 2020 ; Linka 2020a ; Yamahata 2020 ). The characteristics of each of the 62 studies are described in detail in the 'Characteristics of included studies' and summarised below.
We contacted eight study authors requesting additional information. We did not identify any ongoing studies.
Included studies
The 62 studies included in the analysis are described in the following sections.
We found studies that evaluated or simulated travel‐related control measures in a range of countries across the globe, representing all WHO regions. Countries included Australia ( Adekunle 2020 ; Costantino 2020 ; Liebig 2020 ; McLure 2020 ), Bahrain ( Al‐Qahtani 2020 ), Brunei ( Wong J 2020 ), China, including Hong Kong and Macao ( Chen J 2020 ; Chen T 2020 ; Lio 2020 ; Pinotti 2020 ; Kwok 2020 ; Wells 2020 ; Wong MC 2020 ; Yang 2020 ; Zhang L 2020 ), France ( Lagier 2020 ), Germany ( Hoehl 2020 ), Greece ( Lytras 2020 ), India ( Mandal 2020 ), Ireland ( Grannell 2020 ), Japan ( Arima 2020 ; Yamahata 2020 ), Kenya ( Kivuti‐Bito 2020 ), Lebanon ( Deeb 2020 ), Malaysia ( Shaikh Abdul Karim 2020 ), Mauritius ( Nuckchady 2020 ), New Zealand ( Binny 2020 ; James 2020 ; Steyn 2020 ; Wilson 2020 ), Saudi Arabia ( Al‐Tawfiq 2020 ), Singapore ( Chen T 2020 ; Ng 2020 ), South Korea ( Boldog 2020 ; Kim 2020 ; Ryu 2020 ), Switzerland ( Sruthi 2020 ), Taiwan ( Chen Y‐H 2020 ), Thailand ( Boldog 2020 ), UK ( Clifford 2020b ; Taylor 2020 ), and the USA ( Boldog 2020 ; Davis 2020 ; Nowrasteh 2020 ; Odendaal 2020 ). Ten studies assessed measures implemented across multiple countries using a cross‐country comparison ( Anderson 2020 ; Chinazzi 2020 ; Kang 2020 ; Linka 2020a ; Nakamura 2020 ; Russell TW 2020 ; Shi 2020 ; Utsunomiya 2020 ; Zhang C 2020 ; Zhong 2020 ), while eight modelling studies did not refer to a specific country or setting ( Anzai 2020 ; Ashcroft 2020 ; Bays 2020 ; Clifford 2020a ; Dickens 2020 ; Gostic 2020 ; Quilty 2020 ; Russell WA 2020 ). Most studies specified a travel‐related control measure that restricted travel from China ( Adekunle 2020 ; Anzai 2020 ; Boldog 2020 ; Chen J 2020 ; Chinazzi 2020 ; Costantino 2020 ; Davis 2020 ; Hoehl 2020 ; Kang 2020 ; Lagier 2020 ; Liebig 2020 ; Lio 2020 ; Mandal 2020 ; McLure 2020 ; Ng 2020 ; Nowrasteh 2020 ; Odendaal 2020 ; Pinotti 2020 ; Ryu 2020 ; Shaikh Abdul Karim 2020 ; Shi 2020 ; Kwok 2020 ; Wells 2020 ). Other regions restricted by the travel measures assessed in the studies were Australia ( Wilson 2020 ), Bahrain ( Al‐Tawfiq 2020 ), Canada ( Al‐Tawfiq 2020 ), Dubai ( Al‐Tawfiq 2020 ), Egypt ( Al‐Tawfiq 2020 ), Indonesia ( Liebig 2020 ), Iran ( Adekunle 2020 ; Kim 2020 ; Liebig 2020 ; Shaikh Abdul Karim 2020 ), Ireland ( Grannell 2020 ), Italy ( Adekunle 2020 ; Al‐Tawfiq 2020 ; Liebig 2020 ; Shaikh Abdul Karim 2020 ; Wong MC 2020 ), Japan ( Wong MC 2020 ), Oman ( Al‐Tawfiq 2020 ), Singapore ( Chen J 2020 ), South Korea ( Adekunle 2020 ; Liebig 2020 ), Spain ( Al‐Tawfiq 2020 ; Lytras 2020 ), the UK ( Al‐Tawfiq 2020 ; Lytras 2020 ), and the USA ( Al‐Tawfiq 2020 ; Linka 2020b ).
Sixty‐two studies assessed the impact of travel‐related control measures in relation to COVID‐19 ( Adekunle 2020 ; Al‐Qahtani 2020 ; Al‐Tawfiq 2020 ; Anderson 2020 ; Anzai 2020 ; Arima 2020 ; Ashcroft 2020 ; Banholzer 2020 ; Bays 2020 ; Binny 2020 ; Boldog 2020 ; Chen J 2020 ; Chen T 2020 ; Chen Y‐H 2020 ; Chinazzi 2020 ; Clifford 2020a ; Clifford 2020b ; Costantino 2020 ; Davis 2020 ; Deeb 2020 ; Dickens 2020 ; Gostic 2020 ; Grannell 2020 ; Hoehl 2020 ; James 2020 ; Kang 2020 ; Kim 2020 ; Lagier 2020 ; Liebig 2020 ; Linka 2020a ; Linka 2020b ; Lio 2020 ; Kivuti‐Bito 2020 ; Lytras 2020 ; Mandal 2020 ; McLure 2020 ; Nakamura 2020 ; Ng 2020 ; Nowrasteh 2020 ; Nuckchady 2020 ; Odendaal 2020 ; Pinotti 2020 ; Quilty 2020 ; Russell TW 2020 ; Russell WA 2020 ; Ryu 2020 ; Shaikh Abdul Karim 2020 ; Shi 2020 ; Sruthi 2020 ; Steyn 2020 ; Taylor 2020 ; Utsunomiya 2020 ; Kwok 2020 ; Wells 2020 ; Wilson 2020 ; Wong J 2020 ; Wong MC 2020 ; Yamahata 2020 ; Yang 2020 ; Zhang C 2020 ; Zhang L 2020 ; Zhong 2020 ).
Intervention and comparisons
Included studies referred to a range of travel‐related control measures, which we classified into four categories.
- Travel restrictions reducing or stopping cross‐border travel: studies in this intervention category used models to simulate COVID‐19 outbreak scenarios ( Adekunle 2020 ; Anderson 2020 ; Anzai 2020 ; Banholzer 2020 ; Binny 2020 ; Boldog 2020 ; Chen T 2020 ; Chinazzi 2020 ; Costantino 2020 ; Davis 2020 ; Deeb 2020 ; Grannell 2020 ; Kang 2020 ; Liebig 2020 ; Linka 2020a ; Linka 2020b ; McLure 2020 ; Nakamura 2020 ; Nowrasteh 2020 ; Odendaal 2020 ; Pinotti 2020 ; Russell TW 2020 ; Shi 2020 ; Sruthi 2020 ; Utsunomiya 2020 ; Kwok 2020 ; Wells 2020 ; Yang 2020 ; Zhang C 2020 ; Zhang L 2020 ; Zhong 2020 ). The control measures were often simulated as different levels of reduction in travel volume (e.g. 25% and 75% ( Adekunle 2020 ; Anderson 2020 ; Anzai 2020 ; Boldog 2020 ; Chinazzi 2020 ; Linka 2020a )). While in practice this may imply a border closure or restriction of travel to varying degrees, such a differentiation would be arbitrary based on the methods used in the studies to simulate these measures. We therefore report these in a combined intervention category.
- Screening at borders: studies in this intervention category comprised observational studies and modelling studies reporting data on symptom/exposure‐based screening at borders (e.g. presence of cough and/or fever and/or risk factors ( Al‐Qahtani 2020 ; Bays 2020 ; Clifford 2020b )) and/or test‐based screening at borders (e.g. PCR testing ( Clifford 2020b ; Taylor 2020 )) ( Al‐Qahtani 2020 ; Al‐Tawfiq 2020 ; Arima 2020 ; Bays 2020 ; Chen J 2020 ; Clifford 2020a ; Clifford 2020b ; Dickens 2020 ; Gostic 2020 ; Hoehl 2020 ; Lagier 2020 ; Lio 2020 ; Kim 2020 ; Lytras 2020 ; Mandal 2020 ; Ng 2020 ; Nuckchady 2020 ; Quilty 2020 ; Russell WA 2020 ; Shaikh Abdul Karim 2020 ; Steyn 2020 ; Taylor 2020 ; Wells 2020 ; Wilson 2020 ; Wong J 2020 ; Yamahata 2020 ). This intervention category included screening with a follow‐up measure, such as testing, self‐isolation or refusal of entry, only for those who screened positive. While a few studies explicitly highlighted the presence of this follow‐up measure, many did not. In some of the observational studies, screening is followed by a 14‐day quarantine, but in intervention category 2 we treat this quarantine period as a way to identify ‘true’ cases, rather than as an intervention in its own right; relevant studies are also included in intervention category 4.
- Quarantine: modelling studies in this intervention category assessed voluntary or government‐mandated quarantine of travellers of different duration without any accompanying or follow‐up measures, such as symptom/exposure‐based screening or testing upon arrival or at certain days of the quarantine ( Ashcroft 2020 ; Chen T 2020 ; Chen Y‐H 2020 ; Clifford 2020b ; Dickens 2020 ; James 2020 ; Kivuti‐Bito 2020 ; Russell WA 2020 ; Ryu 2020 ; Steyn 2020 ; Taylor 2020 ; Wong MC 2020 ).
- Quarantine and screening at borders: the modelling and observational studies in this intervention category reported data on the combination of quarantine of travellers crossing national borders and screening at borders and/or at different days during quarantine (e.g. day 3, 5, 7, and 14) ( Al‐Qahtani 2020 ; Arima 2020 ; Ashcroft 2020 ; Bays 2020 ; Chen J 2020 ; Clifford 2020b ; Russell WA 2020 ; Shaikh Abdul Karim 2020 ; Steyn 2020 ; Taylor 2020 ; Wilson 2020 ). In the observational studies, after the combined 14‐day quarantine and applied screening measures, there is a final PCR testing before release; in intervention category 4 we treat this final PCR testing as a way to identify ‘true’ cases, rather than as an intervention in its own right.
Some included studies were inconsistent and sometimes ambiguous in how they labelled and described travel‐related control measures. The terms “screening” and “testing”, for example, were used inconsistently and often interchangeably without further specification of the procedures. In this review, we use the term “screening” more broadly to refer to any procedure to assess an individual for a potential disease, including an assessment of symptom, exposure and/or testing. Where data allows, we have differentiated between entry and/or exit symptom/exposure‐based screening alone (i.e. screening for symptoms, such as fever or cough and/or screening for risk factors or when “screening” was used without further specification of the procedures) and entry and/or exit test‐based screening. With regard to testing, most studies specified reverse transcription PCR testing (RT‐PCR testing – also referred to as quantitative PCR and a method to measure the amount of RNA) or simply PCR testing, while a few studies did not specify the testing procedure at all. In this review, we use the term "PCR testing" for consistency. Similarly, studies were inconsistent in the use of the terms “quarantine” and “(self‐)isolation” and often used them interchangeably. In this review, we therefore use the term “quarantine” to refer to the separation of travellers at risk of developing the disease, and “(self‐)isolation” to refer to the separation of confirmed cases ( HHS 2020 ).
For this review, we identified the following intervention‐comparator pairs.
- Travel‐related control measure (intervention) versus no travel‐related control measure (comparator)
- Maintaining travel‐related control measure (intervention) versus relaxing travel‐related control measure (comparator)
- More stringent travel‐related control measure (intervention) versus less stringent travel‐related control measure (comparator)
- Earlier travel‐related control measure (intervention) versus later travel‐related control measure (comparator)
- Combined travel‐related control measure (intervention) versus single travel‐related control measure (control)
Since, in most modelling studies, the comparison to the travel‐related control measure was a scenario in which the measure was not implemented, we have used one 'Summary of findings' table per intervention category to describe the evidence and have not split the evidence based on different comparators used. Meanwhile, we have developed a separate 'Summary of findings' table for the evidence from the modelling studies comparing combined measures with a single measure.
In the observational studies concerned with screening and travel‐related quarantine, the comparison was the counterfactual of not implementing the measure.
We included studies assessing three broad categories of primary outcomes.
- Cases avoided due to the measure
- Shift in epidemic development
- Cases detected due to the measure
For category 1, we identified specific outcomes related to the number or proportion of cases in the community ( Anderson 2020 ; Banholzer 2020 ; Binny 2020 ; Chen T 2020 ; Chen Y‐H 2020 ; Costantino 2020 ; Deeb 2020 ; Kang 2020 ; Linka 2020a ; Nowrasteh 2020 ; Kwok 2020 ; Wong MC 2020 ; Yang 2020 ; Zhang C 2020 ; Zhong 2020 ), the number or proportion of imported or exported cases ( Adekunle 2020 ; Anzai 2020 ; Chen T 2020 ; Chinazzi 2020 ; Costantino 2020 ; Dickens 2020 ; Liebig 2020 ; McLure 2020 ; Russell TW 2020 ; Wells 2020 ), the number or proportion of cases seeded by imported cases ( Dickens 2020 ; James 2020 ; Ryu 2020 ), the probability of an imported case not infecting anyone ( James 2020 ), the number or proportion of deaths ( Binny 2020 ; Costantino 2020 ; Kwok 2020 ), the risk of importation or exportation ( Nakamura 2020 ; Shi 2020 ; Zhang L 2020 ), and the proportion of secondary cases ( Dickens 2020 ).
For category 2, we identified outcomes related to the probability of eliminating the epidemic ( Binny 2020 ), the effective reproduction number ( Linka 2020a ; Sruthi 2020 ), the time to outbreak ( Anzai 2020 ; Clifford 2020a ; Davis 2020 ; Grannell 2020 ; Linka 2020b ; Kivuti‐Bito 2020 ; Mandal 2020 ; Nuckchady 2020 ; Odendaal 2020 ; Wilson 2020 ; Zhong 2020 ), the risk of an outbreak ( Anzai 2020 ; Boldog 2020 ; Nuckchady 2020 ), the number or proportion of cases at peak ( Binny 2020 ; Grannell 2020 ), the epidemic growth acceleration ( Utsunomiya 2020 ), and the exportation growth rate ( Pinotti 2020 ).
Finally, for category 3, we identified outcomes related to days at risk of transmission ( Clifford 2020b ; Russell WA 2020 ), the number or proportion of cases detected ( Al‐Qahtani 2020 ; Al‐Tawfiq 2020 ; Arima 2020 ; Bays 2020 ; Chen J 2020 ; Gostic 2020 ; Hoehl 2020 ; Kim 2020 ; Lytras 2020 ; Ng 2020 ; Quilty 2020 ; Shaikh Abdul Karim 2020 ; Taylor 2020 ; Wilson 2020 ; Wong J 2020 ; Yamahata 2020 ), the positive predictive value (PPV) ( Arima 2020 ; Chen J 2020 ; Hoehl 2020 ; Kim 2020 ; Lytras 2020 ; Ng 2020 ; Yamahata 2020 ), and the probability of releasing an infected individual into the community ( Ashcroft 2020 ; Clifford 2020b ; Steyn 2020 ).
It should be noted that studies were also inconsistent in how they described the specific outcomes. In our classification of the specific outcomes within the broader outcome categories, we have used general terms to enable consistent reporting. For example, we use “outbreak” as a broad term to describe the outcomes labelled in specific studies as “occurrence major epidemic” ( Anzai 2020 ), “beginning of community transmission” ( Davis 2020 ), or “epidemic arrival” ( Zhong 2020 ).
We identified four studies reporting on secondary outcomes related to infectious disease transmission and healthcare utilisation ( Ashcroft 2020 ; Chen Y‐H 2020 ; Steyn 2020 ; Kwok 2020 ). For the first category, one study reported on the probability of cases seeded by infected front‐line workers at quarantine facilities ( Steyn 2020 ), and two studies reported on the number of people quarantined ( Ashcroft 2020 ; Chen Y‐H 2020 ), with one of the studies reporting a metric defined as “utility of quarantine” and measured as a ratio between the amount of overall transmission prevented and the number of person days spent in quarantine ( Ashcroft 2020 ). For the second category of outcomes, we identified one study reporting on the date on which hospital capacity is reached ( Kwok 2020 ).
We identified 49 modelling studies across the four intervention categories ( Adekunle 2020 ; Anderson 2020 ; Anzai 2020 ; Ashcroft 2020 ; Banholzer 2020 ; Bays 2020 ; Binny 2020 ; Boldog 2020 ; Chen T 2020 ; Chen Y‐H 2020 ; Chinazzi 2020 ; Clifford 2020a ; Clifford 2020b ; Costantino 2020 ; Davis 2020 ; Deeb 2020 ; Dickens 2020 ; Gostic 2020 ; Grannell 2020 ; James 2020 ; Kang 2020 ; Liebig 2020 ; Linka 2020a ; Linka 2020b ; Kivuti‐Bito 2020 ; Mandal 2020 ; McLure 2020 ; Nakamura 2020 ; Nowrasteh 2020 ; Nuckchady 2020 ; Odendaal 2020 ; Pinotti 2020 ; Quilty 2020 ; Russell TW 2020 ; Russell WA 2020 ; Ryu 2020 ; Shi 2020 ; Sruthi 2020 ; Steyn 2020 ; Taylor 2020 ; Utsunomiya 2020 ; Kwok 2020 ; Wells 2020 ; Wilson 2020 ; Wong MC 2020 ; Yang 2020 ; Zhang C 2020 ; Zhang L 2020 ; Zhong 2020 ). Modelling studies varied in the employed modelling approaches; details are presented in the 'Characteristics of included studies' .
We identified 13 observational studies assessing symptom/exposure‐based screening at borders ( Al‐Qahtani 2020 ; Al‐Tawfiq 2020 ; Arima 2020 ; Chen J 2020 ; Hoehl 2020 ; Kim 2020 ; Lagier 2020 ; Lio 2020 ; Lytras 2020 ; Ng 2020 ; Shaikh Abdul Karim 2020 ; Wong J 2020 ; Yamahata 2020 ). Four of these observational studies also assessed quarantine and screening at borders ( Al‐Qahtani 2020 ; Arima 2020 ; Chen J 2020 ; Shaikh Abdul Karim 2020 ).
Risk of bias and quality of included studies
The risk of bias (observational studies) and quality (modelling studies) of included studies is summarised in Table 5 and Table 6 ; these summaries are stratified by intervention type, consistent with the narrative synthesis.
We assessed risk of bias of observational studies concerned with screening or quarantining travellers using QUADAS‐2. Table 1 shows that the selection of the traveller population (D1) was associated with a mix of low, unclear and high risk of bias, the index test (D2) was associated with either low or unclear risk of bias, the reference test (D3) was associated with a mix of low, unclear and high risk of bias, and the flow (D4) was generally associated with an unclear risk of bias. The population generally comprised all passengers (for studies of evacuation flights) or all travellers arriving at the airport of interest (for studies of real‐world screening approaches); concerns related to instances where aspects such as symptom status determined entry into the study (e.g. febrile travellers are refused boarding), or how travellers were treated prior to the study (e.g. where travellers are quarantined prior to travel, and symptomatic travellers are filtered out prior to the study beginning). Uncertainty around the index test existed where studies did not provide a clear threshold for categorising travellers as symptomatic. The reference test approach varied among studies, and included, for example, PCR testing upon arrival and/or symptom observation during a 14‐day quarantine period with PCR testing of those who developed symptoms, PCR testing of all individuals regardless of symptom status, and/or PCR testing at the end of the quarantine period. Concerns with risk of bias were related to whether the combination of measures considered the reference test was likely to detect all infected individuals (so that individuals in asymptomatic or presymptomatic states would be discovered) or whether the intensity of the reference test was dependent on symptom status (e.g. where individuals with symptoms were tested more often and at a later stage of the quarantine period than asymptomatic individuals). Regarding the flow, an underestimation of the effect could occur if individuals were infected after the screening took place, for example during quarantine; here the risk depends on the specific quarantine facility and procedures, and these were often not well described. Individual judgements for each study can be found in Appendix 8 .
Furthermore, the QUADAS‐2 tool facilitates an assessment of the applicability of the studies; overall we had substantial concerns regarding the applicability of most studies, notably those conducted for evacuation flights or during a cruise ship outbreak. Thus, it is unclear how applicable the findings regarding these specific populations and screening programmes would be to more generic entry and exit screening measures aiming to screen larger numbers of travellers over an extended period of time. In contrast, we do not have major concerns regarding the applicability of findings derived from the three studies examining larger‐scale screening programmes implemented indiscriminately to all arriving international travellers at an airport ( Al‐Qahtani 2020 ; Al‐Tawfiq 2020 ; Wong J 2020 ). Individual judgements related to applicability for each study can be found in Appendix 8 .
We appraised the quality of modelling studies using the above‐described bespoke tool. Ratings for each study are found in Table 2. Studies varied widely with regards to quality, although some patterns emerged. For example, in several studies, there were concerns regarding the appropriateness of structural assumptions and input parameters (Q2 and Q4), as well as regarding an inadequate assessment of uncertainty (Q9). A major concern with the structural assumptions of a model could involve, for example, treating travel restrictions implemented in multiple countries at different times as independent of time, place and context, or making unrealistic assumptions about who travels and when they could become infected. Input parameters could be a major concern when a study assumed the sensitivity of a symptom/exposure‐based entry screening measure to be 80% or of a PCR testing upon arrival to be 100%, when most empirical results suggest that these values are much lower. Major concerns with the assessment of uncertainty occurred when, for example, studies provided no assessment of whether altering the assumptions of the model influenced the results. Additionally, many studies did not conduct any validation of their models (Q5‐Q8), although we did not consider this a critical flaw that would lead to ‘major concerns’. Importantly, many studies did not undertake any external validation (i.e. a validation on any collected data), which we considered important with respect to the directness of the findings (see Assessment of the certainty of evidence). Individual judgements for each study can be found in Appendix 9 .
Effects of interventions/results of the synthesis
We present the effects on specific outcomes in each of the three broad outcome categories, i.e. cases avoided due to the measure, shift in epidemic development, and cases detected due to the measure.
In the following, we provide a detailed narrative summary of the impact of four broad categories of travel‐related control measures.
- Travel restrictions reducing or stopping cross‐border travel (includes evidence from modelling studies only)
- Screening at borders (includes evidence from modelling and observational studies)
- Quarantine (includes evidence from modelling studies only)
- Quarantine and screening at borders (includes evidence from modelling and observational studies)
For each intervention‐outcome we have structured the results as follows: a full summary of findings, including a narrative summary of the effects, potential effect moderators, as well as the certainty of evidence, which can be found in the corresponding 'Summary of findings tables'. This information is also more concisely summarised in the text below. All data from the individual studies underlying these summaries can be found in the corresponding Appendices ( Appendix 10 ; Appendix 11 ; Appendix 12 ; Appendix 13 ; Appendix 14 ).
Given that potential effect moderators were generally only assessed in individual studies (for modelling studies) or were based on limited data (for observational studies), we aimed to be cautious in our description of these below, and these data should be interpreted with caution. Although we could not explicitly assess how methodological and contextual differences across studies impacted the results, we consider these very important, and they should be kept in mind when interpreting the results described below.
1. Travel restrictions reducing or stopping cross‐border travel
We identified 31 modelling studies contributing evidence to travel restrictions reducing or stopping cross‐border travel, modelling different levels of reduction in travel volume. Twenty‐two studies reported on the cases avoided due to the measure, 12 studies on the shift in epidemic development, and no studies on the cases detected due to the measure. A study‐by‐study overview of the evidence contributing to each of these outcomes is presented in Appendix 10 . Table 1 presents the GRADE 'Summary of findings' for this body of evidence. While we observed a largely consistent, usually positive direction of effect, we assessed the certainty of evidence for all of these outcomes as low or very low because of risk of bias (quality), indirectness, and imprecision in the bodies of evidence.
1.1. Outcome category: cases avoided due to the measure
Number or proportion of cases in the community.
Thirteen modelling studies reported on the number or proportion of cases ( Anderson 2020 ; Banholzer 2020 ; Binny 2020 ; Chen T 2020 ; Costantino 2020 ; Deeb 2020 ; Kang 2020 ; Linka 2020a ; Nowrasteh 2020 ; Kwok 2020 ; Yang 2020 ; Zhang C 2020 ; Zhong 2020 ) (very low‐certainty evidence). Ten of these studies reported reductions in the number or proportion of cases resulting from various travel restrictions ( Anderson 2020 ; Banholzer 2020 ; Binny 2020 ; Chen T 2020 ; Costantino 2020 ; Deeb 2020 ; Kang 2020 ; Linka 2020a ; Kwok 2020 ; Zhong 2020 ). These positive effects ranged from a 1.8% (95% confidence interval (CI) ‐21.9% to 17.5%) reduction in Binny 2020 to a 97.8% reduction in Kang 2020 . The remaining three studies reported mixed effects, observing that a positive effect, but also no effect or even a negative effect were possible ( Nowrasteh 2020 ; Yang 2020 ; Zhong 2020 ). Insights from specific studies highlight aspects that may influence the magnitude of effect of implementing or relaxing travel restrictions. Effects were dependent, for example, on the level of community transmission ( Anderson 2020 ; Kwok 2020 ), the implementation of community‐based interventions, such as a stay‐at‐home order, extensive testing and contact tracing ( Binny 2020 ), and the countries restricted by the measure, with the most effective measures being those that prevented passengers from exiting regions or countries with high community transmission, such as Wuhan, China and Italy in the early stages of the pandemic ( Zhong 2020 ).
Number or proportion of imported or exported cases
Nine modelling studies reported on the number or proportion of imported or exported cases ( Adekunle 2020 ; Anzai 2020 ; Chen T 2020 ; Chinazzi 2020 ; Costantino 2020 ; Liebig 2020 ; McLure 2020 ; Russell TW 2020 ; Wells 2020 ) (very low‐certainty evidence). Eight of these reported reductions in importations or exportations ( Anzai 2020 ; Chen T 2020 ; Chinazzi 2020 ; Costantino 2020 ; Liebig 2020 ; McLure 2020 ; Russell TW 2020 ; Wells 2020 ). These positive effects ranged from an 18% reduction ( Liebig 2020 ) to a 99% reduction ( Chen T 2020 ) in importations or exportations. One study reported mixed effects, observing both positive effects and no effect ( Adekunle 2020 ). Insights from specific studies suggest reasons for the observed variation in the magnitude and direction of effect. For example, earlier implementation of restrictions was shown to lead to more pronounced reductions ( Liebig 2020 ). Travel volumes also played a role, with the proportion of countries in which imports would have contributed to over 10% of cases ranging from 56% to 75%, depending on whether flight volumes during the pandemic, in the hypothetical absence of travel restrictions, were assumed to be similar to previous years, or substantially lower ( Russell TW 2020 ). The magnitude and direction of effect varied with the countries under study ( Adekunle 2020 ), and the comprehensiveness and severity of the measure implemented ( Costantino 2020 ; McLure 2020 ).
Number or proportion of deaths
Three modelling studies reported on the number or proportion of deaths ( Binny 2020 ; Costantino 2020 ; Kwok 2020 ). All these studies reported reductions in deaths (very low‐certainty evidence). These positive effects ranged from a 4.3% (95% CI ‐39.1% to 39.1%) reduction ( Binny 2020 ) to a 98% reduction in deaths ( Costantino 2020 ). Several aspects described in specific studies may contribute to this variation. For example, the effects were reported to depend on the presence or absence of community‐based interventions, such as a stay‐at‐home order, extensive testing, and contact tracing (e.g. 1187 deaths when implementing quarantine of incoming travellers and border closure (except to returning residents and citizens) only and 23 deaths when implementing these interventions followed by other community‐based measures ( Binny 2020 )). Travel restrictions implemented at higher and lower levels of community transmission led to only a slightly different proportion of deaths avoided (14% and 12% reductions, respectively ( Kwok 2020 )).
Risk of importation or exportation
Three modelling studies assessed the risk of importation or exportation of cases ( Nakamura 2020 ; Shi 2020 ; Zhang L 2020 ) (very low‐certainty evidence). Two of these studies reported reductions in the risk of importing and/or exporting cases, however without providing effect estimates ( Nakamura 2020 ; Zhang L 2020 ). One study reported an increased risk of importation at some airports, but decreased risk at other airports around the world as a result of loosening travel restrictions ( Shi 2020 ). One study suggested that the country’s connectedness to the international travel network and the level of community transmission are likely to play a role in the effects ( Nakamura 2020 ).
1.2 Outcome category: shift in epidemic development
Probability of eliminating the epidemic.
One modelling study assessed the probability of eliminating the epidemic ( Binny 2020 ). The study reported mixed effects on the probability of eliminating the epidemic: the probability would be higher (66%) for border restrictions followed by strict community measures than for a delayed border closure (55% probability), and the same as early implementation of border restrictions, such as quarantine of incoming travellers (66% probability) (very low‐certainty evidence). The effect of these travel restrictions were suggested to depend on the existence of community‐based interventions, such as a stay‐at‐home order, extensive testing, and contact tracing (0% probability of eliminating the epidemic when implementing travel restrictions without community measures).
Effective reproduction number
Two modelling studies reported on changes in the effective reproduction number (R t ) ( Linka 2020a ; Sruthi 2020 ) (very low‐certainty evidence). One study reported a beneficial change (i.e. break point) in R t after the implementation of travel restrictions in European Union countries (mean duration time to the inflection point: 12.6 days) ( Linka 2020a ). The other study reported mixed effects ( Sruthi 2020 ), reporting that complete border closures would lead to a 0.045 reduction in R t , partial relaxation through the opening of land borders would lead to a 0.177 increase in R t , while further relaxation allowing for international travel followed by quarantine upon arrival would not lead to a change in R t .
Time to outbreak
Six modelling studies assessed the time to outbreak ( Anzai 2020 ; Davis 2020 ; Grannell 2020 ; Linka 2020b ; Odendaal 2020 ; Zhong 2020 ) (very low‐certainty evidence). Four of these studies reported reductions in the time to outbreak ( Anzai 2020 ; Davis 2020 ; Linka 2020b ; Odendaal 2020 ) (very low‐certainty evidence). These positive effects ranged from a delay of less than one day ( Anzai 2020 ) to 85 days ( Linka 2020b ). Two studies reported mixed effects, suggesting both positive effects and no effect ( Grannell 2020 ; Zhong 2020 ). In specific studies, magnitude and direction of effects were reported to depend on the presence or absence of community‐based interventions and the level of community transmission (e.g. delays of 58 and 85 days for R t =1.35 and R t =1.16, respectively ( Linka 2020b )), the timing of the implementation (e.g. travel restrictions imposed on China implemented one week earlier would have led to an additional delay in community transmission ( Davis 2020 )), and the countries restricted by the measure, with the most effective measures being those that prevented passengers from exiting regions or countries with high levels of community transmission, such as Wuhan (China) and Italy in the early stages of the pandemic ( Zhong 2020 ).
Risk of outbreak
Two modelling studies assessed the risk of an outbreak ( Anzai 2020 ; Boldog 2020 ) (very low‐certainty evidence). One study reported reductions in the risk of an outbreak resulting from travel restrictions with effects ranging from 1% to 37% reductions ( Anzai 2020 ). The other study reported mixed effects, including both a positive effect and no effect ( Boldog 2020 ). As the studies demonstrate, the variation in the magnitude and direction of effect might be explained by methodological differences between studies, as well as differences in the levels of community transmission, the number of cases in the country of departure, the severity of the travel restriction, co‐interventions, and the percentage of contacts being traced. For example, larger effects were found for lower R 0 and higher proportion of contacts traced ( Anzai 2020 ). Similarly, at lower numbers of cases in China, 25%, 50%, and 75% travel reductions resulting from restrictions implemented in Canada yielded a risk of a major outbreak of 35%, 30% and 15%, respectively; at higher numbers of cases in China, these risks were 80%, 70%, and 45%, respectively ( Boldog 2020 ).
Number or proportion of cases at peak
Two modelling studies reported on the number of daily cases at the epidemic peak ( Binny 2020 ; Grannell 2020 ). Both studies reported reductions in the number or proportion of cases at peak (low‐certainty evidence). These positive effects ranged from a 0.3% reduction ( Grannell 2020 ) to a 8% reduction ( Grannell 2020 ). As reported in the studies, the magnitude of effect is likely to vary with the implementation of effective community‐based interventions, such as a stay‐at‐home order, extensive testing, and contact tracing (e.g. 47,592 daily cases at peak when implementing quarantine of incoming travellers and border closure only and 80 cases when implementing these interventions followed by other community‐based measures ( Binny 2020 ).
Epidemic growth acceleration
One modelling study assessed the epidemic growth acceleration ( Utsunomiya 2020 ). It reported that international travel controls would lead to a decrease in the growth acceleration of the epidemic progression across 62 countries (−6.05% change, P < 0.0001) (low‐certainty evidence).
Exportation growth rate
One modelling study assessed the exportation growth rate ( Pinotti 2020 ). The results suggested that both the lockdown of Hubei, resulting in a ban of all travel, as well as travel restrictions on China as a whole, led to a decrease in the growth rate of cases exported from Hubei and the rest of China to the rest of the world (low‐certainty evidence).
1.3 Outcome category: cases detected due to the measure
No studies were found to contribute evidence to this outcome category.
1.4 Secondary outcomes
We identified one modelling study contributing evidence to travel restrictions reducing or stopping cross‐border travel on secondary outcomes related to healthcare utilisation ( Kwok 2020 ). This study shows that even with border closure between Hong Kong in China in place, with higher levels of community transmission (R t = 2.2) hospitals were predicted to reach capacity by the end of March 2020. Only with low community transmission (R t = 1.6), were hospitals predicted not to reach capacity.
2. Screening at borders
We identified 13 modelling studies contributing evidence to screening at borders, with screening modelled to reflect symptom/exposure‐based screening or test‐based screening and only those screening positive receiving a follow‐up measure, such as self‐isolation or refusal of entry. Two studies reported on the cases avoided due to the measure, four studies on the shift in epidemic development, and seven studies on the cases detected due to the measure. A study‐by‐study overview of the evidence contributing to each of these outcomes is presented in Appendix 11 .
Additionally, 13 observational studies reported data on screening at borders. All these reported data only on cases detected due to the measure. A study‐by‐study overview of the evidence contributing to these outcomes, including a description of the approaches to identify cases and study data is presented in Appendix 12 .
Table 2 presents the GRADE 'Summary of findings' for this body of evidence. Here we have separated bodies of evidence that reported on symptom/exposure‐based screening at borders (screening for symptoms such as fever or cough and/or screening for risk factors, or when “screening” was used without further specification of the procedures) and test‐based screening at borders (specifically PCR testing, when specified). While we observed a mostly consistent and positive direction of effect, we assessed the certainty of evidence for all of the outcomes as moderate (one outcome only), low, or very low because of risk of bias (quality), indirectness, and imprecision in the bodies of evidence.
2.1. Outcome category: cases avoided due to the measure
Symptom/exposure‐based screening at borders.
We identified one modelling study assessing the impact of symptom/exposure‐based screening at borders on the cases avoided due to the measure ( Wells 2020 ).
Proportion of cases exported
One modelling study assessed the number or proportion of cases exported ( Wells 2020 ). The results suggested that putting screening measures in place across the world would reduce the number of cases exported per day from China would be reduced by 82% (95% CI 72% to 95%), under the assumption of only 35.7% of symptomatic individuals being detected (moderate‐certainty evidence).
Test‐based screening at borders
We identified one modelling study assessing the impact of test‐based screening at borders on the cases avoided due to the measure ( Dickens 2020 ).
Proportion of secondary cases
One modelling study examined the proportion of secondary cases due to international travel ( Dickens 2020 ). PCR testing all incoming travellers upon arrival, followed by isolation of test‐positives and requiring a negative test at the end of the isolation would lead to a reduction in secondary cases of 88% (95% CI 87% to 89%) for a 7‐day isolation period and 92% (95% CI 92% to 93%) for a 14‐day isolation period (very low‐certainty evidence).
Proportion of imported cases
One modelling study assessed the proportion of imported cases ( Dickens 2020 ). PCR testing all incoming travellers upon arrival, followed by isolation of test‐positives and requiring a negative test at the end of the isolation would lead to a reduction of 90% of imported cases for a 7‐day isolation period and 92% for a 14‐day isolation period (very low‐certainty evidence). Testing all incoming travellers and refusing entry to test‐positives would lead to a reduction of 77%.
2.2 Outcome category: shift in epidemic development
We identified four modelling studies assessing the impact of symptom/exposure‐based screening at borders on the shift in epidemic development ( Clifford 2020a ; Mandal 2020 ; Nuckchady 2020 ; Wilson 2020 ).
Four modelling studies assessed time to outbreak ( Clifford 2020a ; Mandal 2020 ; Nuckchady 2020 ; Wilson 2020 ). All studies reported that entry and/or exit screening alone would delay an outbreak (very low‐certainty evidence). These positive effects ranged from 2.7‐day delay (from 45 days to 47.7 days in reaching 1000 cases) ( Mandal 2020 ) to 0.5‐year delay (from 1.7 years (95% CI 0.04 to 6.09) to 2.2 years (95% CI 0.6 to 8.11)) ( Wilson 2020 ). Insights from specific studies highlight aspects that may influence the magnitude of effect of entry and/or exit screening. For example, effects were reported to depend on the timing of the implementation ( Clifford 2020a ), the number of incoming travellers ( Wilson 2020 ), the percentage of asymptomatic travellers screened ( Mandal 2020 ), and the sensitivity of the screening (e.g. entry or exit screening with a sensitivity of 64% would delay an outbreak by 9.7 days, while screening with a sensitivity of 100% would delay an outbreak by 20 days ( Nuckchady 2020 )).
Risk of outbreak
One modelling study assessed the risk of outbreak ( Nuckchady 2020 ). The results suggested that under the assumption of one infected person entering Mauritius per 100 days, entry screening with 100% sensitivity would reduce the probability of an outbreak within 3 months to 10% and screening with 50% sensitivity would reduce the probability to 48% (low‐certainty evidence).
We did not identify any study assessing the impact of test‐based screening at borders on the shift in epidemic development.
2.3 Outcome category: cases detected due to the measure
We identified four modelling studies ( Bays 2020 ; Gostic 2020 ; Quilty 2020 ; Taylor 2020 ) and nine observational studies ( Al‐Qahtani 2020 ; Arima 2020 ; Chen J 2020 ; Hoehl 2020 ; Kim 2020 ; Lytras 2020 ; Ng 2020 ; Wong J 2020 ; Yamahata 2020 ) assessing the impact of symptom/exposure‐based screening at borders on the cases detected due to the measure.
Number or proportion of cases detected
Four modelling studies reported on the number or proportion of cases detected ( Bays 2020 ; Gostic 2020 ; Quilty 2020 ; Taylor 2020 ). All studies reported reductions in the number or proportion of cases detected (very low‐certainty evidence). These positive effects ranged from detecting 0.8% (95% CI 0.2% to 1.6%) of cases ( Taylor 2020 ) to detecting 53% (95% CI 35% to 72%) cases ( Quilty 2020 ). Insights from specific studies suggest relevant sources of variation in the magnitude of effect. For example, the number or proportion of cases detected was reported to be influenced by the time window in which the exposure may have occurred and the duration of the flight with longer flights increasing the likelihood that symptoms develop during the flight and thus are detected ( Bays 2020 ). The effects were also reported to depend on the percentage of asymptomatic cases in the population ( Gostic 2020 ), the combination of entry and exit screening measures ( Gostic 2020 ; Quilty 2020 ), and the sensitivity of screening (e.g. assuming a sensitivity of 86% for thermal scanner‐based screening and 17% of asymptomatic cases being undetectable, entry and exit screening combined and entry screening alone would both detect 53% (95% CI 35% to 72%) of cases ( Quilty 2020 )).
Proportion of cases detected and positive predictive value
Nine observational studies provided data on symptom/exposure‐based screening at borders (e.g. focused on the presence of fever and/or cough and/or shortness of breath) ( Al‐Qahtani 2020 ; Arima 2020 ; Chen J 2020 ; Hoehl 2020 ; Kim 2020 ; Lytras 2020 ; Ng 2020 ; Wong J 2020 ; Yamahata 2020 ); each of these measures also involved subsequently quarantining all travellers for fourteen days, independent of whether these were screened positively or negatively, usually with some form of symptom observation and sometimes further testing. For this body of evidence on symptom/exposure‐based screening at borders, however, this quarantine period is not considered part of the intervention, but instead serves as a way to identify the ‘true’ number of cases in the study population; although even with these features, false positives and false negatives remain possible. These studies reported data, which allowed for the calculation of the proportion of cases detected by the measure. Six of these studies also reported data allowing for the calculation of the positive predictive value (PPV) ( Arima 2020 ; Hoehl 2020 ; Kim 2020 ; Lytras 2020 ; Ng 2020 ; Yamahata 2020 ).
The proportion of cases detected by the screening measure varied widely (very low‐certainty evidence). One study reported that the measure detected 100% of cases ( Kim 2020 ); this was, however, an outlier, with the rest of studies reporting substantially lower proportions of cases detected. The proportion of cases detected by symptom/exposure screening is summarised in Figure 2 (top panel). The PPV, calculated only for studies assessing symptom/exposure screening, also varied widely between studies (very low‐certainty evidence).
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Summary of the proportions of cases detected by the measure from observational studies. Measures portrayed include exit and/or entry screening (top panel) and PCR tests (middle panel), as well as for combined measures exit and/or entry screening with quarantine and further screening, in the form of symptom observation and/or PCR tests (bottom panel).
Yamahata 2020 employed a form of symptom screening aboard a cruise ship, thus representing a very different context than all other studies.
Ng 2020 employed a delayed PCR test on day 3.
Lagier 2020 and Lio 2020 employed a PCR test on arrival and on day 2, respectively, however given that they did not identify cases they are not portrayed in this figure.
The five evacuation flights assessed in Shaikh Abdul Karim 2020 had very different COVID‐19 prevalences, with no cases associated with three flights, but with 2/104 and 80/124 on the remaining two flights.
The individual screening measures themselves and the context in which they were implemented are important aspects to consider in interpreting these results. Among the symptom/exposure screening measures, the screening approaches (e.g. screening for fever, for any kind of respiratory symptoms and/or for contact with COVID‐19 cases in the past days with these measures being performed prior to departure, upon arrival, or both), as well as the approaches for determining cases vary across studies, and for many studies it was unclear what threshold was used for determining whether an individual was symptomatic. Most studies reported on measures implemented in very specific settings, i.e. either as part of evacuation flights or on a cruise ship, while only two studies assessed national‐level border control measures ( Al‐Qahtani 2020 ; Al‐Tawfiq 2020 ).
We identified three modelling ( Clifford 2020b ; Russell WA 2020 ; Steyn 2020 ) studies and five observational studies ( Al‐Qahtani 2020 ; Al‐Tawfiq 2020 ; Arima 2020 ; Ng 2020 ; Shaikh Abdul Karim 2020 ) assessing the impact of entry and/or exit test‐based screening on the cases detected due to the measure.
Days at risk of transmitting the infection into community
Two modelling studies reported on the days that travellers, upon release, remain at risk of transmitting the infection into the community ( Clifford 2020b ; Russell WA 2020 ). Both studies reported that a single PCR test upon arrival would reduce the days at risk of transmission (low‐certainty evidence). These positive effects ranged from 0.1 fewer days ( Clifford 2020b ) to 0.3 fewer days at risk of transmission ( Russell WA 2020 ).
Probability of releasing an infected individual into the community
Two modelling studies reported on the probability of releasing an infected individual into the community ( Clifford 2020b ; Steyn 2020 ). Both studies reported reductions in the probability of releasing an infected individual into the community as a result of PCR testing (low‐certainty evidence). These positive effects included a risk ratio of 0.55 (95% CI 0.28 to 0.83) ( Clifford 2020b ) and probabilities of releasing an infected individual ranging from 48% to 53% for scenarios with different risks of transmission while travelling ( Steyn 2020 ).
Five observational studies provided data on PCR testing ( Al‐Qahtani 2020 ; Al‐Tawfiq 2020 ; Arima 2020 ; Ng 2020 ; Shaikh Abdul Karim 2020 ); four studies conducted the test within 24 hours, one study after a delay of three days ( Ng 2020 ). Each measure also involved subsequently quarantining all travellers for fourteen days, independent of whether these tested positively or negatively, usually with some form of symptom observation and a test for all individuals at the end of the quarantine period. As described for the symptom/exposure screening above, for this body of evidence this quarantine period is not considered part of the intervention, but instead serves as a way to identify the ‘true’ number of cases in the study population, although even with these features, false positives and false negatives remain possible. These studies reported data, which allowed for the calculation of the proportion of cases detected by the measure; two further studies ( Lagier 2020 ; Lio 2020 ), which conducted tests within 24 hours and two days after arrival, respectively, identified no cases, meaning that we could not report the proportion of cases detected.
The proportion of cases detected by testing varied (58.3% to 90.2%) (low‐certainty evidence). The proportion of cases detected by testing is summarised in Figure 2 (middle panel). The PPV was not calculated for studies assessing PCR testing, as those with a positive PCR test at a given point were considered true cases; no data were available to determine false positives.
The individual testing measures themselves and the context in which they were implemented are important aspects to consider in interpreting these results. For example, the proportions of cases detected for Al‐Qahtani 2020 and Shaikh Abdul Karim 2020 were 58.3% and 90.2%, respectively. The prevalences differed, however, with 188 of 2714 (6.9%) and 82 of 432 (19.0%), respectively, being infected. Looking further, Shaikh Abdul Karim 2020 examined five flights; no cases were identified for three of the flights, while 2 of 104 (2.0%) and 80 of 124 (65.0%) on the remaining two flights. The screening approaches varied somewhat, for example with respect to timing of test provision; most of the studies tested within the first 24 hours, while one study tested on day 3 ( Ng 2020 ). Most studies reported on measures implemented in very specific settings, i.e. as part of evacuation flights, while only two studies assessed national‐level border control measures ( Al‐Qahtani 2020 ; Al‐Tawfiq 2020 ).
3. Quarantine
We identified 12 modelling studies assessing the quarantine of travellers alone, comprising voluntary or government‐mandated quarantine of travellers of different duration without any accompanying or follow‐up measures. Six studies reported on cases avoided due to the measure, one study on the shift in epidemic development, and five studies on the cases detected due to the measure. A study‐by‐study overview of the evidence contributing to each of these outcomes is presented in Appendix 13 . Table 3 presents the GRADE summary of findings for this body of evidence. While we observed a consistent, largely positive direction of effect, we assessed the certainty of evidence for all of the outcomes as low or very low because of risk of bias (quality), indirectness, and imprecision in the bodies of evidence.
3.1 Outcome category: cases avoided due to the measure
Three modelling studies examined the number or proportion of cases ( Chen T 2020 ; Chen Y‐H 2020 ; Wong MC 2020 ). All studies reported reductions in the number or proportion of cases (very low‐certainty evidence). These positive effects ranged from 450 fewer cases in Wong MC 2020 to 64,028 fewer cases in Chen T 2020 during the first wave of the pandemic. Insights from specific studies suggest that the effects might depend on the target group (e.g. quarantining all inbound travellers versus only those that are symptomatic, with the former predicting larger reductions in the number of cases ( Chen T 2020 )).
One modelling study assessed the proportion of imported cases ( Dickens 2020 ). The study reported that quarantining all incoming travellers would reduce the proportion of imported cases by 55% for a 7‐day quarantine period and by 91% for a 14‐day quarantine period (very low‐certainty evidence).
Number or proportion of cases seeded by imported cases
Three modelling studies reported on the number of cases seeded by imported cases ( Dickens 2020 ; James 2020 ; Ryu 2020 ). All studies reported reductions in the number or proportion of cases seeded by imported cases as a result of quarantine of travellers (very low‐certainty evidence). These positive effects in James 2020 ranged from 26% (95% CI 19% to 37%) reduction to 100% (95% CI 62% to 100%) reduction. Reductions were larger when the quarantine was government‐mandated ( James 2020 ), for the elderly compared with adults ( James 2020 ), and for longer quarantine periods ( Dickens 2020 ).
Probability of an imported case not infecting anyone
One modelling study assessed the probability of an imported case not causing further infections ( James 2020 ). The study reported that a 14‐day self‐isolation of all international arrivals in New Zealand would lead to 4% and 14% increase in the probability that an imported case would not infect anyone among adults and the elderly, respectively. The increase in the probability would be higher when a 14‐day government‐mandated quarantine is required (31% and 36% among adults and the elderly, respectively).
3.2 Outcome category: shift in epidemic development
One modelling study reported on the time to outbreak ( Kivuti‐Bito 2020 ). The study reported that increasing the effectiveness of quarantine of travellers to 80% and 90% from the base case of 75% effectiveness would delay the peak in active cases and deaths by 3.5 and 5.5 days, respectively (low‐certainty evidence).
3.3 Outcome category: cases detected due to the measure
Two modelling studies assessed the days that travellers will be at risk of transmitting the infection into the community ( Clifford 2020b ; Russell WA 2020 ). Both studies reported reductions in the numbers of days at risk of transmission resulting from quarantine (low‐certainty evidence). These positive effects ranged from 0.1 fewer days ( Clifford 2020b ) to 2.1 fewer days at risk ( Clifford 2020b ). The studies reported that the variation in the magnitude of effect might be explained by the length of quarantine with longer quarantine periods predicting larger effect (e.g. 2‐day quarantine: 1.8 days at risk (95% CI 1.6 to 2.2); 14‐day quarantine: 0.53 days at risk (95% CI 0.46 to 0.60) ( Russell WA 2020 )).
Proportion of cases detected
One modelling study examined the proportion of cases detected ( Taylor 2020 ). The study reported that requiring travellers to quarantine upon arrival in the UK would lead to detecting different proportion of cases, with the magnitude increasing with the number of days in quarantine (7‐day quarantine: 51% (95% CI 47% to 56%); 14‐day quarantine: 78% (95% CI 74% to 82%)) (very low‐certainty evidence). These proportions are higher than those for screening alone (with either thermal imaging scanners or health checks detecting 0.78% and 1.13% of cases, respectively).
Three modelling studies examined the probability of releasing an infected individual into the community ( Ashcroft 2020 ; Clifford 2020b ; Steyn 2020 ). All studies reported reductions in the risk or probability of releasing an infected individual (very low‐certainty evidence). These positive effects included a risk ratio ranging from 0.00 (95% CI 0.00 to 0.01) to 0.59 (95% CI 0.28 to 0.85) ( Clifford 2020b ) and probabilities of releasing an infected individual ranging from 0% ( Steyn 2020 ) to 85% ( Ashcroft 2020 ). Insights from these studies suggest that the magnitude of effects might depend on the length of the quarantine period ( Clifford 2020b ), duration of travel ( Ashcroft 2020 ), and the risk of transmission within quarantine settings ( Steyn 2020 ).
3.4 Secondary outcomes
We identified three modelling studies contributing evidence to quarantine only on secondary outcomes related to infectious disease transmission ( Ashcroft 2020 ; Chen Y‐H 2020 ; Steyn 2020 ).
One study assessed quarantine utility as a ratio of the amount of transmission prevented to the number of person days spent in quarantine, predicting that for long‐duration travel (i.e. 7 days or longer), shorter quarantine provides a better balance between preventing infection and the burden of quarantining individuals than a longer quarantine duration. For short duration travel, however, this relationship is reversed ( Ashcroft 2020 ). One study reported that with test‐and‐isolation, contact tracing, and general public mask‐wearing and other social measures in place, strict quarantine of travellers (one daily infection imported) in Taiwan would ensure that the number of individuals needed to quarantine in the community remains low (4092) over 90 days. Without quarantine (10 daily infections imported), the number of individuals needed to quarantine in the community would increase steadily (40810) over the same time period ( Chen Y‐H 2020 ). One study suggests that there is a risk that front‐line workers from quarantine facilities seed outbreaks in the community. With weekly testing of front‐line workers from quarantine facilities, there is a high probability that a case will be detected in the front‐line worker as opposed to later in a secondary case in the community. With less frequent or no testing of front‐line workers, the probability increases that the case is not detected until a secondary case is infected ( Steyn 2020 ).
4. Quarantine and screening at borders
We identified seven modelling studies assessing quarantine of travellers and screening at borders and/or at different days during quarantine. No studies reported on the cases avoided due to the measure, one study reported on the shift in epidemic development, and six studies reported on the cases detected due to the measure. A study‐by‐study overview of the evidence contributing to each of these outcomes is presented in Appendix 14 .
Additionally, we found four observational studies contributing data to this intervention category, all reporting on the cases detected due to the measure. A study‐by‐study overview of this evidence, including a description of the approaches to identify cases and study data is presented in Appendix 12 .
Table 4 presents the GRADE summary of findings for this body of evidence. While we observed a consistent and positive direction of effect, we assessed the certainty of evidence for all of these outcomes as low or very low because of risk of bias (quality), indirectness, and imprecision in the bodies of evidence.
4.1 Outcome category: cases avoided due to the measure
4.2 outcome category: shift in epidemic development.
One modelling study reported on the time to outbreak ( Wilson 2020 ). The study reported delays in outbreak resulting from quarantine and screening at borders measures. Under the assumption of one flight per day (7.1% of normal travel volume) and 50% sensitivity, the time to outbreak would vary greatly for different combinations of measures ranging from 3.5 years (95% CI 0.09 to 12.9) to 34.1 years (95% CI 0.86 to 126) to outbreak (very low‐certainty evidence). Combination of measures, such as exit screening, in‐flight wearing of masks, PCR testing of arriving travellers and quarantine would lead to larger delays of outbreak.
4.3 Outcome category: cases detected due to the measure
Two modelling studies assessed days that the travellers remain at risk of transmitting the infection into the community ( Clifford 2020b ; Russell WA 2020 ). Both studies reported that the combination of quarantine and testing would reduce days at risk of transmission compared with single measures (low‐certainty evidence). These positive effects ranged from 0.01 fewer days ( Russell WA 2020 ) to 2.0 fewer days ( Clifford 2020b ) at risk of transmission. Requiring two tests before releasing from quarantine showed slightly improved, yet largely comparable effects to the effects of quarantine with a single test at the end ( Clifford 2020b ).
Three modelling studies assessed the probability of releasing an infected individual into the community ( Ashcroft 2020 ; Clifford 2020b ; Steyn 2020 ) (very low‐certainty evidence). All three studies reported positive effects resulting from a combination of screening and quarantine. These positive effects included a reduction in the probability of releasing an infected individual ranging from 2% to 48% ( Steyn 2020 ). Studies reported that the variation in the magnitude of effect could be explained by the length of quarantine, with shorter periods predicting smaller effects (e.g. risk ratio (RR) for a 3‐day quarantine with a single test: 0.22 (95% CI 0.02 to 0.48) and RR for a 14‐day quarantine with a single test: 0.01 (95% CI 0.00 to 0.03)) ( Clifford 2020b ), the single measure comparison used (e.g. quarantine alone or PCR testing upon entry alone), day(s) on which the test is administered ( Ashcroft 2020 ), or the risk of transmission within quarantine ( Steyn 2020 ).
Two modelling studies reported on the proportion of cases detected ( Bays 2020 ; Taylor 2020 ). Both studies reported that the combination of quarantine and testing would further increase case detection compared with single measures (very low‐certainty evidence). The positive effects ranged from 41% ( Bays 2020 ) to 99% of cases detected ( Bays 2020 ). Adding a second test suggested only a slight improvement in the effect ( Taylor 2020 ). Insights from specific studies suggest that the observed variation in the magnitude of effect may be explained by the length of the quarantine period with longer quarantine increasing the detection rate ( Bays 2020 ; Taylor 2020 ), and the duration of travel and stay in the country of departure ( Bays 2020 ).
Proportion of cases detected and PPV
Four observational studies provided data on quarantine and screening at borders ( Al‐Qahtani 2020 ; Arima 2020 ; Chen J 2020 ; Shaikh Abdul Karim 2020 ). Each of these studies began with an entry and/or exit screening measure, comprising symptom screening in one study ( Chen J 2020 ), and PCR testing upon arrival in the other three. Each measure also involved subsequently quarantining all travellers for fourteen days; all but one study ( Shaikh Abdul Karim 2020 ), additionally monitored symptoms of all travellers and tested those who developed symptoms. Above, we considered this quarantine period as part of the way to identify the ‘true’ number of cases; for this body of evidence, however, we consider this as a ‘combined measures’ intervention. Each study also provided PCR testing before release from quarantine; we treat this final PCR test as the way to identify the ‘true’ number of cases. These studies reported data, which allowed for the calculation of the proportion of cases detected by the measure.
The proportion of cases detected by these combined measures, comprising quarantine of travellers and screening, is summarised in Figure 2 (bottom panel). As visible in the figure, across the studies, in comparison to exit and/or entry screening only, a subsequent 14‐day quarantine period with symptom monitoring and further testing led to the detection of additional cases that would have been missed by the initial screening measure. Only one study did not detect further cases after the initial entry and/or exit screening measure ( Shaikh Abdul Karim 2020 ) (low‐certainty evidence). The PPV was not calculated for studies assessing PCR testing, as those with a positive PCR test at a given point were considered true cases; no data were available to determine false positives.
The individual measures themselves and the context in which they were implemented are important aspects to consider in interpreting these results. As described above, one study employed symptom screening ( Chen J 2020 ), while the others used PCR testing upon arrival. Measures employed during the quarantine itself differed as well; while most studies monitored for the development of symptoms and tested those developing symptoms, one study did not ( Al‐Qahtani 2020 ); the intensity with which symptoms were monitored could also be important. Three studies reported on measures implemented in very specific settings, i.e. as part of evacuation flights, while only one study assessed a national‐level border control measure.
Summary of main findings
To inform decisions on containing the COVID‐19 pandemic, we updated our previous review ( Burns 2020 ), with the aim of identifying and synthesising the evidence on the effectiveness of international travel‐related control measures during coronavirus outbreaks on infectious disease transmission and screening‐related outcomes. We identified a much expanded and heterogeneous evidence base, with studies focusing on a range of real or simulated travel‐related control measures aiming to contain the COVID‐19 pandemic. In the original review we assessed studies on SARS and MERS ( Burns 2020 ); in this update, we focus on COVID‐19 studies.
We found 31 modelling studies on travel restrictions reducing cross‐border travel, all modelling a range of reductions in travel across real or simulated countries. Studies reported on various outcomes related to cases avoided due to the measure and shift in epidemic development. Across outcomes, most studies predicted a positive effect; some studies, however, observed mixed effects, including positive and negative effects. Very low‐ to moderate‐certainty evidence limits our confidence in these findings.
We found 13 modelling studies and 13 observational studies on screening at borders. Screening measures covered symptom/exposure‐based screening and/or PCR test‐based screening before departure or upon or soon after arrival. Regarding symptom/exposure‐based screening at borders, modelling studies assessed several different outcomes related to shift in epidemic development and cases detected due to the measure; observational studies assessed outcomes related to cases detected due to the measure. For all outcomes, the observed findings showed positive effects, although some of these effects were very small; effects were dependent on factors, such as the sensitivity of screening measures. Regarding test‐based screening at borders, modelling studies assessed outcomes related to cases avoided and cases detected due to the measure; observational studies assessed cases detected due to the measure. Across these outcomes, the findings showed positive effects with magnitudes varying, depending, for example, on the timing of testing. Although a wide range of positive effects was observed, these were generally larger than for symptom/exposure‐based screening alone. Very low to moderate‐certainty evidence limits our confidence in the findings on screening measures.
We found 12 modelling studies on quarantine. Studies assessed multiple outcomes related to cases avoided due to the measure, shift in epidemic development and cases detected due to the measure. Included studies all showed positive effects ranging from small to large in magnitude, depending on the quarantine duration and compliance. Very low‐ to low‐certainty evidence limits our confidence in these findings.
We found seven modelling studies and four observational studies on quarantine and screening at borders. Studies assessed outcomes related to shift in epidemic development and cases detected. Most studies showed positive effects for the combined measures with varying magnitudes of effect depending on how the measures were combined (e.g. the length of the quarantine period and days when the test was conducted in quarantine). Very low‐ to low‐certainty evidence limits our confidence in these findings.
Overall completeness and applicability of the evidence
Consistent with the original review ( Burns 2020 ), we identified studies assessing a broad range of travel‐related control measures to contain the COVID‐19 pandemic. They examined outcomes across all three a priori specified outcome categories, and were conducted across multiple world regions. There were, however, some gaps in the evidence base, notably in relation to populations, settings and interventions.
Modelling studies across all categories of travel‐related control measures generally considered nonspecific populations, using data both observed in and/or modelled upon a travelling population or the general population. Observational studies of screening at borders measures used data observed from travelling populations. Many studies assessed or modelled all modes of travel or did not specify the mode (29 studies); air travellers (33 studies) were much more frequently represented than those travelling by ship (one study) or by land (one study).
We identified studies from all world regions. In contrast to the original review, this update also found studies from the African and Eastern Mediterranean regions; however, these make up only a small share of the evidence base (one study and three studies, respectively). Moreover, the screening at borders measures assessed here were largely implemented in very specific settings, such as on evacuation flights or during cruise ship outbreaks. We did, however, further identify three studies from Bahrain, Brunei and Saudi Arabia evaluating measures at a population‐level, such as all travellers arriving at a specific airport or a large population of workers returning from work abroad. While likely more policy‐relevant than those in very specific settings, the populations studied were small, in the order of a few thousand, and an opportunity for undertaking much larger and thus more policy‐relevant studies exists. Importantly, much of the evidence relates to the implementation of travel‐related control measures at the beginning of the COVID‐19 pandemic, although some studies were conducted during later phases.
Intervention
With much of the evidence deriving from modelling studies – notably for travel restrictions reducing cross‐border travel and quarantine of travellers – there is a lack of 'real‐world' evidence for many of these measures. Compared to the original review, however, we identified modelling studies on screening at borders measures that more closely matched the current policy discussions. For example, whereas earlier studies asked very generic questions such as ‘Is screening effective at detecting infected travellers?’, some of the more recent studies asked more nuanced questions such as ‘Does a PCR test upon arrival perform better than symptom/exposure screening?’ and ‘How many more cases are detected if a PCR test is given after a quarantine of 3, 7 or 14 days?’. Consistent with the original review, little evidence was found on the relaxation of travel‐related control measures; as various countries consider when it is safe to lift restrictions, it will be important that studies assessing this aspect are conducted.
Within our primary outcome categories comprising infectious disease transmission and screening‐related outcomes, studies assessed a range of outcomes; notably studies identified in the update addressed a larger number of outcomes. In the original review, studies assessing entry and/or exit screening and quarantine of travellers focused only on the proportion of infected individuals detected by the measure; newer studies also assessed the number of infectious individuals released into the community and the amount of time an individual is infectious after arrival and after being quarantined for different durations. Regarding secondary outcomes, few studies reported on the impact of travel‐related control measures on the number of cases seeded by frontline workers, the number of hospitalisations and the number of individuals needing to quarantine/isolate in the community. While it is possible that travel‐related control measures generate signalling effects in terms of raising general awareness of the risk of infection or deterring effects in terms of stopping sick individuals from travelling, we did not identify any evidence on these outcomes.
No studies included in this update assessed outcomes concerned with the human and financial resources required to implement the measures or adverse effects in terms of health (e.g. isolation), as well as broader social and economic implications (e.g. stigmatisation, inability to work, economic impacts). This represents a major limitation regarding the completeness of the evidence, as this information is important to assess the benefits and harms of the measures. At the outset, it was decided in consultation with the WHO that the most pressing question at this point was the effectiveness of travel‐related control measures and that this review should thus focus primarily on studies assessing effectiveness in relation to infectious disease transmission and screening‐related outcomes; if included studies also reported on harms, these data would also be examined. In addition, a separate, currently ongoing, scoping review of the health, social sciences, and environmental literature seeks to map the various unintended consequences and potential adverse health effects and broader societal harms of travel‐related control measures (osf.io/7gyxe).
It is important to note that this is a fast‐moving research field. Since 13 November 2020, when we conducted our searches, we have identified two further relevant studies: a modelling study on border screening approaches using various testing strategies in the USA ( Kiang 2020 ), and an observational study of test‐based screening at borders using PCR testing in New Zealand ( Swadi 2021 ). As these studies were not identified as part of our systematic searches and were published after the final searches, we have not incorporated them into this review. They highlight, however, that the evidence base is growing further, and that a future update will be important.
Sources of heterogeneity
As part of the narrative synthesis, we documented potential sources of heterogeneity that may have influenced intervention effectiveness. Modelling studies across all intervention categories differed in the methods they employed and they assessed a broad range of potential factors.
- COVID‐19 pandemic: studies suggest that the level of community transmission in both the implementing country and the restricted country, and the proportion of asymptomatic cases play a role.
- Broader context of travel: the baseline number of travellers, the interconnectedness of the region with the travel measure in place and the restricted region, how much flight volumes are likely to rebound in the absence of restrictions, the duration the duration of travel and stay in the country of departure were all relevant factors.
- Other public health measures: whether other public health measures, such as a stay‐at‐home order and testing and contact tracing, are in place in the region where the travel measure is implemented.
- Implementation of the intervention: factors related to the earlier or later timing of implementation of the intervention, the exact specification of the intervention (e.g. duration of quarantine of travellers), and compliance with the measures all influenced effectiveness.
Looking across the observational studies of screening at borders, the measure itself was important, with test‐based screening generally performing better than symptom‐ or exposure‐based screening. There were also some differences regarding the timing of the interventions; while most measures were implemented at departure or immediately upon arrival, a few studies assessed testing within one or two days of arrival.
Certainty of the evidence
The certainty of evidence was moderate for one outcome and either low or very low for the rest of the body of evidence we assessed; thus, we cannot be confident in the findings. The true effects may therefore be (or are likely to be) substantially different from the estimates of effect described. We downgraded the certainty of evidence due to risk of bias (for observational studies) and quality (for modelling studies), as well as for imprecision and indirectness.
Observational studies contributed evidence to the intervention categories entry and/or exit screening alone and quarantine of travellers alone. We judged most domains across observational studies assessing entry and/or exit screening measures to be at low or unclear risk of bias. There were, however, some studies at high risk of bias for the selection of travellers, the reference test and the flow (where, for example, travellers were potentially infected while in quarantine, after the screening took place).
Modelling studies contributed evidence to all four intervention categories. Although modelling studies differed in quality, most bodies of evidence comprising modelling studies were downgraded due to serious concerns about the quality of the modelling. Quality concerns were diverse, but included inappropriate or unrealistic assumptions related to model structure and input data, the lack of assessment of uncertainty and incomplete technical documentation. Problematic assumptions for any of these aspects could lead to results that do not reflect reality and are thus of limited utility.
We used four reasons for downgrading evidence based on imprecision. We downgraded evidence for imprecision when a body of evidence comprised a single modelling study, as it limits our confidence in the predictions being a precise estimate of true effects, or when multiple studies provided a wide range of plausible effects (e.g. no effect versus large reductions in the number of cases). Furthermore, a few of the modelling studies provided no estimates of effect (e.g. data presented in a diagram or a map), and many studies provided estimates of effect (e.g. 85 deaths avoided) with insufficient information on the precision of the estimates. Given the nature of the data and models, it is plausible that the uncertainty in estimates is wide, and such information would be necessary for an appropriate interpretation of the study findings.
In this update, we applied two reasons for downgrading based on indirectness. Where exit and entry screening measures were implemented in very specific settings, such as on evacuation flights or during cruise ship outbreaks, we considered this as indirect evidence with regards to informing more general entry and exit screening measures at national borders. Additionally, for bodies of evidence based on modelling studies, we downgraded evidence for indirectness when there was no external validation of the model(s), as it created uncertainties in assessing how directly the model outputs relate to real‐world outcomes and consequently to our review question. External validation may be challenging, especially in the context of a pandemic, but it is important to ensure that the findings are generalisable to the real‐world situation.
Potential biases in the review process
In this update, consistent with the original review, we applied systematic and transparent methods throughout the phases of the review process. We defined our review objective and scope informed by a previously conducted evidence map ( Movsisyan 2021 ), and in consultation with the WHO – a key end‐user that specifically requested the review to inform WHO recommendations on travel‐related control measures for COVID‐19. Our protocol was reviewed and approved by Cochrane (see Appendix 1 ). In order to describe the emerging evidence in relation to COVID‐19, we included a wide range of study designs and publication types, including modelling studies and preprint publications. We synthesised this evidence narratively, but applied GRADE to assess the certainty of evidence for all primary outcomes. We did, however, encounter challenges in dealing with this complex evidence base, and some decisions we made may have introduced bias into the review process.
Although we used a comprehensive search strategy designed by an information specialist, we conducted searches in only two major databases and two COVID‐specific databases and used specific search terms tightly defined around travel‐related control measures. While our chosen sources include records from a wide range of databases, as well as grey literature, such as preprints, it is possible that our searches missed some studies, especially if these were not appropriately indexed in the journals and preprint servers or conducted in languages other than English (e.g. Chinese literature). This also concerns our lack of findings regarding implementation outcomes and adverse effects. Had we searched a broader range of multi‐disciplinary databases and had we undertaken systematic searches of the grey literature (e.g. government reports), we might have identified some of these outcomes – albeit at the cost of a much lengthier and more complex review process.
As described above, our review included many modelling studies: in the specific context of a global pandemic, models developed to make predictions about the future often represent the only available evidence and are therefore crucial in informing decision making. Many modelling studies did not provide comprehensive reporting of key assumptions and model parameters, which created challenges in assessing their eligibility and validity, for example in decisions on whether the model used disease parameters of relevance for our review. Given the lack of a validated tool to assess the quality of modelling studies, we had developed a bespoke quality appraisal tool and implemented two post‐protocol adaptations in the original review. We used the same tool for the present update, and also included modelling experts within our review team throughout the review process.
We applied a structured method for the narrative synthesis that relied on defining the direction of effect for each individual study, drawing on recent guidance for conducting synthesis without meta‐analysis ( Campbell 2020 ; Hilton Boon 2020 ). We described this method clearly, applied it consistently across all studies, and reported the results consistently for all bodies of evidence. Due to the nature of the outcomes, our consideration of any effect greater than the null effect being potentially relevant, and the analytical methods applied in the included studies, however, this method may bias the results towards a positive effect. A study that evaluates the proportion of infected travellers detected by a screening measure, for example, will always detect some proportion of infected travellers greater than zero; this means that a screening measure detecting 1% of cases would be considered a 'positive' effect, as it detects a higher proportion of cases than would be detected with no measure in place (0%). By reporting effect ranges and providing the underlying data, however, we have aimed to be clear and transparent that some 'positive' effects are very small.
Further, we experienced some difficulty with applying the GRADE guidance for assessing the certainty of evidence based on modelling studies ( Brozek 2021 ). Most importantly, because it does not offer guidance for operationalising the assessment of risk of bias/quality, indirectness, imprecision, inconsistency and publication bias for a body of evidence comprising multiple models. Notably, applying the criteria of inconsistency and imprecision were challenging. With inconsistency, it was challenging because travel‐related control measures by design generally show at least a slight positive or no effect, not a negative effect. With imprecision, it was challenging because high‐quality models vary and use a large number of parameters and scenarios, often leading to wide confidence intervals; poor‐quality models do not even allow for an assessment of imprecision due to lack of reporting of effect estimates or confidence intervals. Furthermore, we used external model validation as a key criterion to help in our judgments on indirectness of the evidence, which is, however, not currently specified and operationalised as such in the GRADE guidance on modelling studies. Finally, it should be noted that there are simply more opportunities for larger bodies of evidence to be downgraded than those with only a small number of contributing studies, or even only one contributing study, as additional studies were likely to contribute further issues on risk of bias, indirectness, and imprecision to the body of evidence. Thus, a body of evidence with one study, for example, could potentially be assessed as moderate‐certainty evidence, while a body of evidence with 13 contributing studies had very little chance of being assessed as higher than very low‐certainty evidence.
While we made a case in the review for the methodological and contextual differences across the studies to impact the results and for their consideration when interpreting the review findings, we were not able to formally assess these potential moderators through subgroup analyses. Our statements regarding these moderators were therefore largely based on their assessment in individual modelling studies or limited data from observational studies.
Finally, we used abridged procedures of systematic reviewing at certain stages, to enable rapid completion of this review. Specifically, we did not conduct double data extraction and assessment of risk of bias or quality appraisal. However, we had a second experienced review author check all the extracted and appraised data, and discussed and resolved any uncertainties with the wider team. With a large author team potentially introducing heterogeneity in the process, we set up smaller groups of review authors working on each specific task (e.g. screening, extraction) to minimise inconsistencies. We also organised calls and discussions within these groups, where needed, to discuss any issues and harmonise the review process.
Implications for practice
This review suggests that travel‐related control measures during the COVID‐19 pandemic may have a positive impact on infectious disease transmission and screening‐related outcomes. However, the certainty of evidence included in this rapid review is moderate to very low, due to the nature as well as quality and heterogeneity of available studies. Therefore, true effects may be substantially different from those reported here. Broadly, travel restrictions reducing or stopping cross‐border travel may limit the spread of disease across national borders. Regarding screening at borders, symptom/exposure screening on its own will detect some COVID‐19 cases. However, it would likely not detect a large enough proportion of cases to prevent seeding new cases within the region protected by the measure. In comparison, PCR testing as a screening measure detects more cases than symptom/exposure screening, although if performed only upon arrival will likely also miss a significant proportion of cases. The effectiveness of quarantine is dependent on high compliance and the length of quarantine, with longer periods such as 10 or 14 days preventing most cases from being released into the community. Combining quarantine with screening at borders is likely to meaningfully improve the effectiveness.
Travel‐related control measures target one specific source of SARS‐CoV‐2 transmission, that is, human travel. The importance of this source of transmission in influencing overall epidemic development depends on a variety of factors, including the degree of interconnectedness between countries (in terms of the number and nature of borders as well as travel volumes) and the levels of community transmission in the region restricted and the region protected by the measure (i.e. high versus low levels of community transmission). Similarly, the contribution of travel‐related control measures to controlling the COVID‐19 pandemic will depend on the specification of these measures regarding their design and stringency (e.g. single polymerase chain reaction (PCR) testing versus repeated PCR testing; 7‐day quarantine versus 14‐day quarantine), the target group (e.g. all travellers versus specific groups), which borders or means of transport are affected (e.g. travel by air, land or sea), timing of implementation (measure implemented at an early versus late stage of the epidemic), and combinations with measures during travel (e.g. wearing of masks, hygiene, physical distancing). Importantly, the degree of adherence and, where applied, enforcement of the measure (e.g. recommendation to quarantine, various forms of control, fine and magnitude of fine) are also likely to play an important role. Finally, the contribution of travel‐related control measures to controlling the COVID‐19 pandemic will also depend on a range of other measures implemented to control community transmission (e.g. testing, contact tracing, social distancing measures, wearing face masks) in the region restricted and the region protected by the measure.
As the pandemic progresses, decision‐makers can implement/increase or de‐implement/loosen a range of potentially appropriate measures, and, in doing so, consider the above described factors. Importantly, travel‐related control measures affect health and society in much broader ways, and decisions will need to balance all benefits and potential harms associated with a specific measure (not assessed in this review).
Implications for research
Decision‐makers need high‐quality research that helps to inform the decisions they continually have to make to chart the course through the COVID‐19 pandemic. Research should be responsive to the questions most urgently raised by decision‐makers, such as how can travel restrictions help delay a next wave of infections, how do screening at borders and quarantine need to be specified and enforced to optimise benefit‐harm balance, and at what point is it safe to relax travel‐related control measures. It would also be important that studies continue to refine the assessment of factors that influence the effectiveness of travel‐related control measures, such as the stage of the pandemic and steps taken to increase or enforce implementation and adherence. Some of these questions may be answered quickly by refining existing models. Although measures such as border closures are, by nature, challenging if at all possible to evaluate using internally valid experimental or quasi‐experimental approaches, the pandemic presents an opportunity to explore how critical questions can be answered through rigorous data collection and analysis.
In observational studies assessing the effectiveness of border screening measures and quarantine of travellers, it would be helpful to go beyond evaluating single measures. If, for example, all arriving travellers are quarantined for 14 days, it provides a cohort and an opportunity to assess not only the benefit of this quarantine, but also of a range of other single and combined measures, such as 3‐day, 5‐day and 8‐day quarantine or PCR testing upon arrival, at day 3, day 5 or day 8. Additionally, related to outcomes it would be critical to look beyond the number of cases detected and to consider the number of cases missed, as well as the impact of these measures on the spread and development of the epidemic in the community. As many governments currently employ travel‐related control measures, as well as a range of other public health measures to contain the pandemic, moving forward, it would be important to assess various combinations of these measures to identify those that are most effective. This concerns both primary research and future systematic reviews.
With the evidence base on COVID‐19 and the impact of travel‐related control measures growing quickly, it is important that future modelling studies improve reporting and technical documentation to allow for adequate assessment of their quality. Specific aspects considered in assessing the modelling studies in this review can help inform the development of high quality models. Finally, to ensure that the best available evidence informs complex and evolving decisions, future research should employ a range of epidemiological designs and assessment tools to assess the broad impacts of travel‐related control measures, including all potential benefits and harms from a societal perspective. In order to integrate the rapidly growing evidence base on the topic, as well as new studies that may use more rigorous methods and approaches, we plan to update this review again later in 2021.
Review first published: Issue 9, 2020
Acknowledgements
Disclaimer: This study was undertaken in the context of the COVID‐19 Evidence Ecosystem project, funded by the German Ministry of Education and Research. Some of this article is based on a study commissioned and paid for by the World Health Organization. Copyright in the WHO‐commissioned work belongs to WHO and the authors have been given permission to publish this article. The authors alone are responsible for the views expressed in the publication and these do not necessarily represent views, decisions or policies of the World Health Organization.
This review was partly developed in the framework of the CEOsys project. We would like to thank those involved at CEOsys for their support (covid-evidenz.de/).
We would like to thank Robin Featherstone for designing and running the searches in the databases, Brigitte Strahwald for comments and suggestions to improve the manuscript and Hacsi Horvath for copyediting the review. We would also like to acknowledge the excellent guidance and continuous support from Rachel Marshall, Toby Lasserson, Helen Wakeford and Clare Dooley throughout the review process. Finally, we would like to thank Carmen Dolea and Thomas Hoffmann for their input into review scoping.
We would like to thank the following peer referees, who each devoted their time to provide very valuable feedback on this systematic review:
Referee 1: Michael Smalle, Information Specialist, Cochrane
Referee 2: Miranda Cumpston, Editor, Cochrane Public Health School of Medicine and Public Health, University of Newcastle, Australia
Referee 3: Liz Bickerdike, Associate Editor, Editorial & Methods Department
Referee 4: Jonathan M Samet, Colorado School of Public Health
Referee 5 (consumer): Dulce Stevao
Appendix 1. Review protocol
Travel‐related control measures to contain the covid‐19 pandemic: a rapid review (protocol).
Burns J* 1,2 , Movsisyan A* 1,2 , Biallas R 1,2 , Coenen M 1,2 , Emmert‐Fees K 3 , Geffert K 1,2 , Hoffmann S 1,2 , Horstick O 4 , Pfadenhauer LM 1,2 , von Philipsborn P 1,2 , Sell K 1,2 , Strahwald B 1,2 , Stratil JM 1,2 , Voss S 1,2 , Rehfuess EA 1,2
* These authors have contributed equally.
1 Institute for Medical Information Processing, Biometry and Epidemiology; Ludwig‐Maximilians‐University Munich, Germany 2 Pettenkofer School of Public Health, Munich, Germany 3 Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH) 4 Heidelberg Institute of Global Health, Heidelberg University, Germany
The first case of the novel coronavirus disease 2019 (COVID‐19) was observed in Wuhan (Hubei province of China) in December 2019. Over the following days and weeks, the disease spread further into China and countries of Asia, including Japan, South Korea, and Thailand (WHO, 2020a). Close to mid‐March, cases had been reported in over 100 countries across the globe, and on March 11 2020, the World Health Organization (WHO) declared the outbreak a global pandemic (WHO, 2020b)
COVID‐19 is caused by SARS‐CoV‐2 whose characteristics are similar to those of the coronaviruses causing severe acute respiratory syndrome (SARS‐CoV‐1/SARS) and Middle East Respiratory Syndrome (MERS‐CoV/MERS), respectively. However, in comparison with these viruses, SARS‐CoV‐2 has higher transmissibility and lower pathogenicity (Fani, Teimoori, & Ghafari, 2020).
Considering lack of existing vaccines to prevent and pharmacological interventions to treat COVID‐19, a range of non‐pharmacological interventions have been put into place by national and subnational governments to contain and mitigate the spread of the disease. One suite of measures implemented very early during the onset of the COVID‐19 pandemic are travel‐related control measures. These range from measures, such as border screening to more severe measures, such as complete closure of national borders. Starting from February 2020, most countries and regions in the world have implemented some type of travel‐related control measure. As the pandemic develops across the globe, with some countries reporting a beginning second wave of infections (Strzelecki, 2020), it is crucial to understand the effectiveness of these measures to inform decisions on their further (re)implementation, adaptation, relaxation or suspension. This is in line with the International Health Regulations 2005 calling to ground public health decision‐making in scientific evidence (WHO, 2005).
Previous reviews have assessed the effectiveness of travel‐related control measures, such as international travel bans and entry and/or exit screening in the context of SARS and MERS epidemics (Errett, Sauer, & Rutkow, 2020; Mouchtouri et al., 2019). In May 2020, WHO requested the review authors to develop an evidence map charting the evidence of various travel‐related control measures relevant for containing COVID‐19 pandemic. The map identified 122 studies assessing a range of measures undertaken across the globe to address COVID‐19, SARS, MERS, and influenza. Studies used methods ranging from simple observational approaches to complex modelling techniques. This map informed the scope of the present rapid review to synthesize evidence on the effectiveness of travel‐related control measures in coronavirus outbreaks.
To inform WHO recommendations and/or national strategies on travel‐related control measures to contain the COVID‐19 pandemic, this rapid review aims to assess the effectiveness of international travel‐related control measures during coronavirus pandemics on infectious disease and screening‐related outcomes.
To conduct this rapid review, at certain stages we will employ abridged procedures of systematic reviewing according to the Cochrane standard (Garritty et al. 2020). Specifically, only one review author will conduct data extraction and risk of bias assessment of epidemiological studies and quality assessment of modelling studies. A second review author will check for correctness, and any uncertainties will be discussed with a third review author. To ensure that this does not compromise the methodological rigor of the systematic review, but also to ensure that all stages of the review are conducted consistently and correctly, we will assign these data extraction and risk of bias/quality assessment tasks to experienced Cochrane review authors and consult researchers with modelling expertise to assist with the quality assessment of modelling studies. Furthermore, we will pilot the procedures for each stage, conduct regular team meetings and keep a list of rolling questions that will be updated continuously.
Types of studies
In the context of a global pandemic, evidence to inform decisions must be generated rapidly, meaning that methods traditionally used to evaluate impact, such as the randomised controlled trial (RCT), while possible, may not be considered feasible, appropriate, timely or ethical. To ensure that we capture all relevant study types, we will consider a broad range of empirical studies of any size that provide a quantitative measure of impact, including experimental and quasi‐experimental studies, observational studies, and epidemiological and mathematical modelling studies.
Experimental and quasi‐experimental studies
● Randomized controlled trials (RCTs)
● Interrupted time series (ITS) studies
● Controlled before‐after (CBA) studies and difference‐in‐differences (DiD) studies
● Instrumental variable (IV) studies
● Regression discontinuity (RD) studies
Observational studies
● Cohort studies
● Case‐control studies
Mathematical and epidemiological modelling studies
● Compartmental models (e.g. SEIR‐type models comprising multiple compartments, such as S: susceptible, E: exposed, I: infectious, R: recovered)
● Bayesian hierarchical models (i.e. models comprising several submodels to integrate observed data as well as uncertainty)
● Spatial models (i.e. modelling disease transmission spatially)
● Epidemiological models (e.g. time series models that model the temporal nature of disease transmission using time‐series techniques)
This is likely not an exhaustive list of all the relevant experimental, quasi‐experimental, observational, mathematical and epidemiological modelling studies, and ‐ independent of the actual study design ‐ studies use a variety of labels. We will consider all studies providing a quantitative measure of impact, regardless of whether they are indicated by one of these labels.
We will consider studies published in journals as well as those published on preprint servers. We will include RCTs that have been registered but not yet published (in a peer‐reviewed journal or on a preprint server) as “ongoing studies”.
We will exclude the following types of studies and publications:
● Case reports
● Studies that do not provide a quantitative measure of impact (e.g., studies providing a graphical summary of the development of the number of cases over time in relation to the introduction of control measures, qualitative studies)
● Diagnostic studies (e.g. assessing the sensitivity and specificity of different screening tests)
● Non‐empirical studies (e.g. commentaries, editorials, literature reviews not reporting primary empirical data)
● Systematic reviews (although these will be used for backward citation tracking)
● Conference abstracts
Where possible in the compressed time frame, we will consider studies published in all languages. Within the review team, we can consider studies in Armenian, English, French, German, Italian, and Russian. For studies in other languages, we will use existing networks within WHO and Cochrane to support us with screening and/or translation.
Types of participants
We will include human populations (without any age restriction) susceptible to human coronavirus diseases, namely SARS‐CoV‐1/SARS, SARS‐CoV‐2/COVID‐19, and MERS‐CoV/MERS. To be eligible, modelling studies must use modelling parameters for disease transmission specified to reflect one of these diseases.
We will exclude studies (i) not targeting human transmission, (ii) concerned with humans at risk of developing other infectious diseases characterized by different transmission properties (e.g. Ebola and viral meningitis, the transmission mode of which is primarily person‐to‐person rather than airborne), and/or (iii) addressing humans at risk of developing other infectious diseases, for which travel‐related control measures do not play a significant role in containing outbreaks (e.g. influenza).
Types of interventions
We will consider travel‐related control measures affecting human travel across national borders. We will consider both introduction/implementation, as well as relaxation of the following measures:
● Closure of national borders to entry and/or exit
● International travel restrictions/bans
○ Denial of entry and/or exit on the basis of nationality, travel history, health status or other characteristics
○ Full or partial suspension of cross‐border travel via land and/or air and/or sea
○ Visa requirement or refusal on the basis of nationality, travel history, health status or other characteristics
● Entry and/or exit screening at national borders
○ Temperature measurement
○ Health questionnaire (e.g. symptoms, travel history, contact tracing)
○ Thermography
○ Physical examination
○ Testing for current or past infection
○ Passive observation
● Quarantine or isolation of travellers crossing national borders
● Any combination of the above measures
We will exclude the following types of interventions:
● Combinations of the above‐mentioned travel‐related control measures with other measures where studies do not provide effect estimates for the travel‐related control measures (e.g., studies providing a combined effect estimate for suspension of cross border travel and use of mandatory face masks). Studies, where the effect of travel‐related control measures cannot be disentangled from the effect of a broader suite of public health measures, cannot usefully inform WHO recommendations on whether countries should or should not consider travel‐related control measures to contain the COVID‐19 pandemic (see review objective).
● All interventions not directly related to travel, including a range of containment and mitigation measures (e.g., community‐based quarantine, personal protective measures, hygiene measures, bans on mass gatherings and other social‐distancing measures)
● All interventions related to movement of animals or goods
● All interventions concerned with human travel across subnational borders. While subnational measures can potentially inform national travel‐related control measures, these measures are not prioritised by the WHO, and as shown in the previous evidence map, are often impossible to disentangle from other subnational measures, such as lockdowns, community quarantine or social distancing recommendations.
● Travel warnings or travel advice issued by the World Health Organization or national governments
● Interventions solely concerned with the accuracy of tests rather than their implementation as part of an entry and/or exit border control measure
● Usual practice (e.g. seasonal changes to travel) or events (e.g. school holidays) affecting travel but not representing travel‐related control measures
● Cancellation of events affecting international travel but undertaken as a means to prevent mass gatherings (e.g., Hajj, international sporting events, international trade fairs)
There are two Cochrane rapid reviews that may identify overlapping studies. One published review, which is currently being updated, focuses on quarantine measures, including quarantining travellers crossing national borders. The other review, currently under editorial assessment and soon to be published, focuses on screening measures, including entry and/or exit screening at national borders. In discussions with Cochrane and WHO, we decided that it would be important for decision‐makers to be able to access the evidence on all travel‐related control measures in a single review. To address overlap between the present review and the two separately conducted reviews, we will coordinate the synthesis and write‐up of our review findings with those of the quarantine and screening reviews to ensure consistency in reporting and interpretation of the overlapping questions and evidence.
Types of outcomes
Primary outcomes:
We will consider studies assessing any of the following infectious disease transmission and screening‐related outcomes:
● Cases avoided due to the intervention: e.g. number, proportion, rate of cases observed or predicted with and without the intervention
● Number of cases detected due to the intervention: e.g. case detection rate (i.e. number of cases detected per 10,000 screened), positive predictive value (i.e. number of cases detected per those identified as high risk)
● Shift in epidemic development due to the intervention: e.g. probability of epidemic, time to/delay in epidemic arrival or peak, size of epidemic peak, change in the effective reproduction number
Secondary outcomes:
We will consider the following secondary outcomes if identified in studies that assessed at least one of the primary outcomes:
● Any other infectious disease transmission outcomes
● Healthcare utilization (e.g. number of cases requiring ICU treatment, time until ICU capacity is reached)
● Resource requirements for implementing the intervention (e.g. costs associated with intervention, additional personnel, number of tests required)
● Any adverse effects (e.g. health, economic, and social outcomes)
We will not consider studies reporting on other outcomes.
The search strategy will be structured around two blocks focusing on (1) COVID‐19, SARS and MERS and (2) travel‐related control measures. We will conduct the searches in English but will aim to include studies published in any language (see “Language” above). We will adapt the search strategy used in the evidence map for travel‐related control measures. An experienced information specialist will adapt and run systematic searches in the following electronic databases up to the last week of June, 2020:
● Ovid MEDLINE(R) and Epub Ahead of Print, In‐Process & Other Non‐Indexed Citations, Daily and Versions(R) (1946‐present)
● Ovid Embase (1996‐present)
We will additionally search the following COVID‐19‐specific databases:
● Cochrane COVID‐19 Register (https://covid-19.cochrane.org/) , which contain study references from ClinicalTrials.gov, WHO International Clinical Trials Registry Platform (ICTRP), PubMed, medRxiv and other hand‐search articles from publishers’ websites.
● WHO COVID‐19 Global literature on coronavirus disease (https://search.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/), which contains primarily research (published AND/OR prepublication) journal articles from PubMed, Web of Science, Global Index Medicus, Embase. In addition, Lanzhou University submits on a daily basis citations from CNKI as well as a number of Chinese journal publishers.
● CDC COVID‐19 Research Articles Downloadable Database (https://www.cdc.gov/library/researchguides/2019novelcoronavirus/researcharticles.html) will be searched exclusively for preprints from bioRxiv, medRxiv and SSRN.
Finally, we will screen the reference lists of the existing systematic reviews on travel‐related control measures to identify additional eligible studies. See Appendix 1 for the MEDLINE search strategy.
We will develop a standardized title and abstract screening guidance and pilot it among all review authors involved with the screening using the same 100 titles and abstracts. We will discuss and resolve all issues and revise the screening guidance accordingly. One review author will then screen all titles and abstracts. A second review author will screen all excluded abstracts. We will adopt an inclusive approach at this stage, and all unclear studies will be taken forward to the full text screening. Screening all full texts in duplicate, as described below, will ensure that the single screening of titles and abstracts included at this stage will not compromise methodological rigor.
We will conduct a pilot of the full text screening; all review authors involved with full text screening will screen a set of ten studies at the outset of this stage (Garritty et al., 2020). The team will then discuss any open questions or issues, as well as how to harmonize screening across all review authors. Following the pilot, two review authors will screen the remaining studies in duplicate. Any discrepancies between the two review authors will be discussed, and a third review author or the entire author team will be consulted where necessary.
One review author will extract study characteristics and data from all included studies using a data extraction form in Microsoft Excel. All extracted data will be checked by a second review author. The data extraction form will be an extended version of the form used in the previous evidence map. The following categories will be included in the extraction form:
● Study type
● Population, setting and context
● Characteristics of pathogen/disease
● Context of pathogen/disease
● Intervention
● Outcomes and results
We will pilot the data extraction form using five studies meeting the inclusion criteria. Appendix 2 provides the details on the data extraction categories.
Based on our experience with the evidence map on travel related‐control measures, we expect key aspects, notably the description of the intervention and the stage of the epidemic at which the intervention was implemented, to be poorly reported in included studies. Therefore, for included studies addressing COVID‐19 in which data on the intervention and stage of the pandemic are not well reported, and where applicable (e.g., some modelling studies may not directly relate to a specific country or point in time), we will explore whether searching for and extracting additional data from external sources will enhance our understanding of these aspects. To do so, we will consider data from the following external sources:
● Intervention data: the COVID‐19 Country Policy Tracker, offered by the Organisation for Economic Cooperation and Development (OEC), collates and summarizes countries’ policy responses to the current pandemic (https://www.oecd.org/coronavirus/country-policy-tracker/). ACAPS, an independent information provider, also manages a large database of country‐level policy responses (https://www.acaps.org/covid19-government-measures-dataset).
● Stage of the pandemic: Daily COVID‐19 situation reports published by the WHO summarize the daily number of total and new cases and deaths for all countries, and specify the current stage of the pandemic as “sporadic cases”, “clusters of cases” or “community transmission” (https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/).
In cases for which we utilize external sources, we will carefully document the source of the information.
One review author will rate the risk of bias (RoB) or quality of each included study, depending on the type of study, and a second review author will check these ratings; any questions or uncertainties that arise throughout the process will be discussed between these review authors or among the review team.
Given the broad range of study designs, we will apply multiple tools in assessing RoB of included studies. For any identified experimental studies, we will apply the Cochrane RoB 2 tool (Higgins et al., 2019). For quasi‐experimental and observational studies, we will apply ROBINS‐I (Sterne et al., 2016). In applying ROBINS‐I, important confounding factors that each study would ideally control for should be defined a priori. Based on the body of evidence identified in the recently conducted evidence map, we expect that most non‐modelling studies will assess the impact of entry and/or exit screening at national borders on the number of cases detected. For those studies, we outline two important categories of confounding factors:
· Between‐group differences: where multiple groups are assessed (i.e., screened and non‐screened), these regard the composition of the two groups with respect to age, sex, socioeconomic status and other factors potentially affecting the risk of infection (e.g., occupation, stay in a high‐prevalence area);
· Temporal changes: as these studies are longitudinal in nature, changes over time could lead to confounding (i.e. changes to testing practices, training provided to personnel, and changes in implementation of the measures).
No validated tool exists for assessing the RoB of modelling studies, and little consensus exists in the systematic review community about how to best approach the assessment of RoB and/or quality of an individual modelling study. A rapid review of the methodological literature aimed to identify and summarize studies describing criteria for assessing the quality of mathematical studies (Egger et al. 2017). This review identified 20 studies (including Jaime Caro et al., 2014; Philips, Bojke, Sculpher, Claxton, & Golder, 2006) that varied in scope and level of detail regarding the proposed criteria; looking across the identified studies, it suggested that an assessment of the quality of a modelling study should capture the aspects of (i) model structure, (ii) input data, (iii) different dimensions of uncertainty, (iv) transparency, and (v) validation. Following the suggestions by Eggers et al. (2017), we developed a bespoke tool for the assessment of modelling studies and selected single criteria from two studies (Jaime Caro et al., 2014; Philips, Bojke, Sculpher, Claxton, & Golder, 2006). We sought to strike a balance between capturing all five key aspects and feasibility of applying the criteria. The individual criteria we will apply are outlined in Appendix 3 . We will report each of the criteria separately, i.e. we will not combine multiple criteria into a summary score. The quality of modelling studies will be assessed by one review author and checked by a second.
We will synthesize the findings narratively, graphically, or in tabular form, stratified by intervention type and disease. Drawing on the previous evidence map and the large heterogeneity identified with regard to the setting, population, intervention and other contextual factors, we expect that data will not be sufficiently similar to conduct meta‐analyses. Where possible, we will summarize effect estimates graphically using forest plots.
Subgroup analyses and investigation of heterogeneity
In the absence of meta‐analyses, we will not conduct a statistical assessment of heterogeneity, nor will we statistically assess differences between subgroups. We will nevertheless investigate the influence of potentially important sources of heterogeneity on the impact of interventions, focusing on heterogeneity in selected PICO elements and context. We will consider the following sources of heterogeneity:
● Stage of the pandemic during which the intervention was implemented: impact of travel‐related control measures are likely different in countries where only few cases have been detected than in countries where community transmission is established;
● Whether a country is an island state or not: the geographical characteristics of the state (i.e., island state versus countries with land borders) will serve as a proxy measure of the interconnectedness of the countries and explain possible differences in the efficiency in managing and controlling entries/exits at all ports of entry;
● Assumed infectious disease parameters: assumptions used in models may have a large influence on the direction and magnitude of reported results;
● Whether a single travel‐related control measure or a package of travel‐related control measures were implemented: multiple integrated travel‐related interventions may be more effective than single stand‐alone interventions.
We will explore heterogeneity narratively; forest plots and/or tables will be stratified according to these specific aspects. This will not allow us to derive conclusions but could point to aspects potentially modifying the effectiveness of different measures.
Sensitivity analyses
Where a sufficiently homogeneous evidence base is identified, we will conduct sensitivity analyses, excluding experimental, quasi‐experimental, or observational studies at high RoB or modelling studies with major quality concerns, to assess whether these studies influence the results. As for the subgroup analyses described above, in the absence of meta‐analyses, this assessment will be narrative, not statistical, in nature.
We will use the GRADE approach to assess the certainty of the primary outcomes. One review author will collate the evidence per primary outcome and suggest initial certainty of evidence ratings. These will then be further deliberated in a team of review authors and a joint decision for certainty of evidence ratings will be made for each primary outcome.
The certainty of evidence is defined in GRADE as the extent to which one can be confident that the true effect of an intervention lies on one side of a specified threshold, or within a chosen range (Hultcrantz et al., 2017). In this rapid review, we will consider “difference from the null” as an important threshold assuming that even the small effect sizes may be relevant for population‐level travel‐related control measures.
The certainty of evidence rating in GRADE yields four possible levels of evidence: high certainty (i.e., the estimated effect lies close to the true effect), moderate certainty (i.e., the estimated effect is probably close to the true effect), low certainty (i.e., the estimated effect might substantially differ from the true effect), and very low certainty (i.e., the estimated effect is probably substantially different from the true effect).
We will rate bodies of evidence from experimental, observational and modelling studies separately. In GRADE, evidence from RCTs enters the rating as high certainty, as does evidence from observational studies whose risk of bias has been assessed using ROBINS‐I (Schunemann et al., 2019). Further to this five domains are used to further downgrade evidence, including study limitations, inconsistency, indirectness, imprecision and publication bias , and three domains are used to upgrade evidence, including plausible confounding, large estimates of effect, and dose‐response relationship . These domains apply to assessment of evidence from all types of studies, including modelling studies.
To rate certainty of evidence from modelling studies, we will use the recent guidance developed by the GRADE Working Group (Brozek et al.). Evidence from modelling studies also starts the assessment as high certainty, and all the GRADE domains described above are used to assess certainty of model outputs.
Disclaimer: This rapid review is being commissioned and paid for by the World Health Organization (WHO). The authors alone are responsible for the views expressed in this protocol and they do not necessarily represent the views, decisions or policies of WHO.
Brozek, J., Canelo‐Aybar, C., Akl, E. A., Bowen, J. M., Butcher, J., Chiu, W. A., & Cronin, M. GRADE Guidelines 30: the GRADE approach to assessing the certainty of modelled evidence ‐ an overview in the context of health decision‐making. (Draft article).
Egger, M., Johnson, L., Althaus, C., Schoni, A., Salanti, G., Low, N., & Norris, S. L. (2017). Developing WHO guidelines: Time to formally include evidence from mathematical modelling studies. F1000Res, 6 , 1584.
Errett, N. A., Sauer, L. M., & Rutkow, L. (2020). An integrative review of the limited evidence on international travel bans as an emerging infectious disease disaster control measure. Journal of emergency management (Weston, Mass.), 18 (1), 7‐14.
Fani, M., Teimoori, A., & Ghafari, S. (2020). Comparison of the COVID‐2019 (SARS‐CoV‐2) pathogenesis with SARS‐CoV and MERS‐CoV infections. Future Virol. (Epub ahead of print).
Garritty, C., Gartlehner, G., Kamel, C., King, V. J., Nussbaumer‐Streit, B., Stevens, A., . . . Affengruber, L. (2020). Cochrane Rapid Reviews. Interim Guidance from the Cochrane Rapid Reviews Methods Group.
Higgins, J. P. T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. e. (2019). Cochrane handbook for systematic reviews of interventions. (2nd ed.). Chichester (UK): John Wiley & Sons.
Hultcrantz, M., Rind, D., Akl, E. A., Treweek, S., Mustafa, R. A., Iorio, A., . . . Guyatt, G. (2017). The GRADE Working Group clarifies the construct of certainty of evidence. J Clin Epidemiol, 87 , 4‐13.
Jaime Caro, J., Eddy, D. M., Kan, H., Kaltz, C., Patel, B., Eldessouki, R., . . . Forces, I.‐A.‐N. M. C. T. (2014). Questionnaire to assess relevance and credibility of modelling studies for informing health care decision making: an ISPOR‐AMCP‐NPC Good Practice Task Force report. Value Health, 17 (2), 174‐182.
Mouchtouri, V. A., Christoforidou, E. P., An der Heiden, M., Menel Lemos, C., Fanos, M., Rexroth, U., . . . Hadjichristodoulou, C. (2019). Exit and Entry Screening Practices for Infectious Diseases among Travelers at Points of Entry: Looking for Evidence on Public Health Impact. International journal of environmental research and public health, 16 (23), 4638‐4638.
Nussbaumer‐Streit, B., Mayr, V., Dobrescu, A. I., Chapman, A., Persad, E., Klerings, I., . . . Gartlehner, G. (2020). Quarantine alone or in combination with other public health measures to control COVID‐19: a rapid review. Cochrane Database of Systematic Reviews (4).
Philips, Z., Bojke, L., Sculpher, M., Claxton, K., & Golder, S. (2006). Good practice guidelines for decision‐analytic modelling in health technology assessment: a review and consolidation of quality assessment. Pharmacoeconomics, 24 (4), 355‐371.
Schunemann, H. J., Cuello, C., Akl, E. A., Mustafa, R. A., Meerpohl, J. J., Thayer, K., . . . Group, G. W. (2019). GRADE guidelines: 18. How ROBINS‐I and other tools to assess risk of bias in nonrandomized studies should be used to rate the certainty of a body of evidence. J Clin Epidemiol, 111 , 105‐114.
Sterne, J. A., Hernan, M. A., Reeves, B. C., Savovic, J., Berkman, N. D., Viswanathan, M., . . . Higgins, J. P. (2016). ROBINS‐I: a tool for assessing risk of bias in non‐randomised studies of interventions. BMJ, 355 , i4919.
Strzelecki, A. (2020). The second worldwide wave of interest in coronavirus since the COVID‐19 outbreaks in South Korea, Italy and Iran: A Google Trends study. Brain Behav Immun . (Article in press).
WHO. (2005). International Health Regulations. WHA 58.3, 2nd ed. Geneva: World Health Organization.
WHO. (2020a). Novel Coronavirus (2019‐nCoV) Situation Report ‐ 1. World Health Organization.
WHO. (2020b). Novel Coronavirus (2019‐nCoV) Situation Report ‐ 51. World Health Organization.
Protocol Appendix 1. Search Strategy
Database: Ovid MEDLINE(R) and Epub Ahead of Print, In‐Process & Other Non‐Indexed Citations, Daily and Versions(R) 1946 to June 25, 2020
Date search conducted: June 26, 2020
1 exp Coronavirus/ (18064)
2 Coronavirus Infections/ (12378)
3 COVID‐19.rs. (7963)
4 severe acute respiratory syndrome coronavirus 2.os. (6648)
5 (2019 nCoV or 2019nCoV or 2019‐novel CoV).ti,ab,kf. (891)
6 (Coronavir* or corona virus* or Middle East Respiratory Syndrome* or MERS or Severe Acute Respiratory Syndrome* or SARS*).ti,ab,kf. (35015)
7 COVID 19.mp. (25238)
8 (COVID19 or COVID 2019).ti,ab,kf. (437)
9 (nCov 2019 or nCov 19).ti,ab,kf. (55)
10 or/1‐9 [Set 1: Coronaviruses] (51693)
11 Air Travel/ (366)
12 Travel/ (24964)
13 (border? adj3 (clos* or restrict* or control* or measure?)).ab,kf. (1046)
14 ((isolat* or quarantin*) adj6 (exposed or suspected or travel* or airport? or border?)).ti,ab,kf. (7939)
15 ((mobility or movement*) adj2 (reduc* or restrict*)).ti,ab,kf. (9614)
16 (travel* or border?).ti. (26180)
17 (travel adj4 (measure? or intervention? or NPI?)).ab,kf. (424)
18 (travel* adj3 (restrict* or reduc* or control* or limit* or lockdown? or ban*)).ab,kf. (1545)
19 ((questionnaire* or screen* or surveil*) adj4 (traveller? or entr* or exit or border? or airport?)).ti,ab,kf. (1840)
20 visa?.ti,ab,kf. (2086)
21 or/11‐20 [Set 2: Travel measures] (64508)
22 and/10,21 [Sets 1 & 2] (945)
23 limit 22 to "humans only (removes records about animals)" (925)
24 remove duplicates from 23 (916)
Protocol Appendix 2. Categories in the data extraction form
NOTE: aspects in bold will be primarily relevant for modelling studies.
Study information :
- Study title
- Study source (i.e., journal, report, preprint publication)
- Date of submission
- Date of publication
Study type:
- Study type (e.g., experimental, quasi‐experimental, observational, modelling)
- Verbal summary of study type (e.g., stochastic discrete event simulation model)
Population, setting, and context:
- Country in which travel‐related control measure is implemented
- Region protected by travel‐related control measure
- Region restricted by travel‐related control measure
- Short description of population studied (e.g., international travellers including flight passenger and crew members arriving between 27 April and 22 June 2009)
Characteristics of pathogen/disease:
- Disease (i.e., SARS, MERS, COVID‐19)
- Incubation period
- Latent period
- Infectiousness of symptomatic persons
- Infectiousness of asymptomatic persons
Context of pathogen/disease transmission:
- Supporting information for the categorization of the stage of pandemic in the protected and restricted regions (e.g., date of notification of first case, current number of cases, established community spread)
- Reproduction number: (R0) and R(t) for the region protected by the intervention
Intervention:
- Broad measure category (i.e., closure of national borders to entry and/or exit, international travel restrictions/bans, entry and/or exit screening at national borders, quarantine or isolation of travelers crossing national borders)
- Verbal summary of specific travel‐related measure(s) (e.g., all arriving passengers were provided with a symptom questionnaire)
- Date(s) of implementation of the travel‐related control measure(s)
- Any reported exceptions to the measure (e.g., certain individuals being excluded or fast‐tracked in airport screening because of nationality, occupation, country of origin)
- Representation of the intervention in the model (e.g., 100% travel reduction)
Primary outcomes (repeated for each primary outcome):
- Outcome category (i.e., cases avoided due to the intervention, number of cases detected due to the intervention, shift in epidemic development due to the intervention)
- Description of outcome
- Length of follow‐up
- Estimate related to the impact of the travel‐related control measure
- Narrative summary of overall impact of travel‐related control measure
- Additional analysis used to quantify the range of potential effects for this outcome
Secondary outcomes(repeated for each secondary outcome):
- Description of any secondary (non‐quantitative) outcomes (i.e., any other infectious disease transmission outcomes, healthcare utilization outcomes, resource requirements for implementing the intervention, and any adverse effects)
- Narrative results of secondary outcomes
[External sources] Stage of pandemic in protected and restricted regions during which the intervention was implemented (i.e., no cases, sporadic cases, clusters of cases, and community transmission) – for each aspect extracted the exact source will be documented
[External sources] Intervention – for each aspect extracted the exact source will be documented
Protocol Appendix 3. Criteria used for assessing the quality of individual modelling studies
Appendix 2. list of systematic reviews considered for backward citation searches.
- Bielecki M, Patel D, Hinkelbein J, Komorowski M, Kester J, Ebrahim S, et al (2021). Air travel and COVID‐19 prevention in the pandemic and peri‐pandemic period: a narrative review. Travel Medicine and Infectious Disease, 39.
- Bitar D, Goubar A, Desenclos JC. (2009). International travels and fever screening during epidemics: a literature review on the effectiveness and potential use of non‐contact infrared thermometers. Eurosurveillance, 14(6), 854‐8. Retrieved from http://europepmc.org/abstract/MED/19215720. (Accession No. 19215720).
- Browne A, Ahmad SS, Beck CR, Nguyen‐Van‐Tam JS (2016). The roles of transportation and transportation hubs in the propagation of influenza and coronaviruses: a systematic review. Journal of Travel Medicine, 23(1). doi:10.1093/jtm/tav002.
- Burns J, et al (2021). Travel‐related control measures to contain the COVID‐19 pandemic: an evidence map. BMJ Open [accepted manuscript].
- Chetty T, Daniels BB, Ngandu NK, Goga A. (2020). A rapid review of the effectiveness of screening practices at airports, land borders and ports to reduce the transmission of respiratory infectious diseases such as COVID‐19. South African Medical Journal, 110(11), 1105‐1109. doi:10.7196/SAMJ.2020.v110i11.15124.
- Errett NA, Saue LM, Rutkow L. (2020). An integrative review of the limited evidence on international travel bans as an emerging infectious disease disaster control measure. Journal of Emergency Management, 18(1), 7‐14. doi:10.5055/jem.2020.0446.
- Gautret P, Benkouiten S, Al‐Tawfiq JA, Memish ZA. (2016). Hajj‐associated viral respiratory infections: A systematic review. Travel Medicine and Infectious Disease, 14(2), 92‐109. doi:10.1016/j.tmaid.2015.12.008.
- Getaneh Y, Yizengaw A, Adane S, Zealiyas K, Abate Z, Leulseged S, et al. (2020). Global lessons and Potential strategies in combating COVID‐19 pandemic in Ethiopia: Systematic Review. medRxiv, 2020.2005.2023.20111062. doi:10.1101/2020.05.23.20111062.
- Imai N, Gaythorpe AM, Abbott S, Bhatia S, van Elsland S, Prem K, et al. (2020). Adoption and impact of non‐pharmaceutical interventions for COVID‐19 [version 1; peer review: awaiting peer review].
- Jefferson T, Del Mar CB, Dooley L, Ferroni E, Al‐Ansary LA, Bawazeer GA, . . . Conly JM. (2011). Physical interventions to interrupt or reduce the spread of respiratory viruses. Cochrane Database of Systematic Reviews, 2011(7), Cd006207. doi:10.1002/14651858.CD006207.pub4.
- Kang S, Moon J, Kang H, Nam H, Tak S, Cho S. (2020). The Evolving Policy Debate on Border Closure in Korea. Journal of Preventive Medicine and Public Health 53(5), 302‐306. doi:10.3961/jpmph.20.213
- Lahiri A, Jha SS, Bhattacharya S, Ray S, Chakraborty A. (2020). Effectiveness of preventive measures against COVID‐19: A systematic review of In Silico modeling studies in Indian context. Indian Journal of Public Health, 64(Supplement), S156‐s167. doi:10.4103/ijph.IJPH_464_20.
- Mouchtouri VA, Christoforidou EP, An der Heiden M, Menel Lemos C, Fanos M, Rexroth U, et al (2019). Exit and Entry Screening Practices for Infectious Diseases among Travelers at Points of Entry: Looking for Evidence on Public Health Impact. International Journal of Environmental Research and Public Health, 16(23). doi:10.3390/ijerph16234638.
- Nussbaumer‐Streit B, Mayr V, Dobrescu AI, Chapman A, Persad E, Klerings I, et al (2020). Quarantine alone or in combination with other public health measures to control COVID‐19: a rapid review. Cochrane Database of Systematic Reviews(9). doi:10.1002/14651858.CD013574.pub2.
- Patino‐Lugo DF, Velez M, Velasquez Salazar P, Vera‐Giraldo CY, Velez V, Marin IC, et al. (2020). Non‐pharmaceutical interventions for containment, mitigation and suppression of COVID‐19 infection. Colombia medica (Cali, Colombia), 51(2), e4266. doi:10.25100/cm.v51i2.4266.
- Shah SA, Mansor J, Nurumal SR, Wan Ibadullah WAH, Mohammad Z, Rosli NM, Singh PJ. (2020). Rapid response and public health measures of COVID‐19 infection among Asian countries. Gazi Medical Journal, 31(2 A), 239‐243. doi:10.12996/gmj.2020.63.
- Tabari P, Amini M, Moghadami M, Moosavi M. (2020). International public health responses to COVID‐19 outbreak: A rapid review. Iranian Journal of Medical Sciences, 45(3), 157‐169. doi:10.30476/ijms.2020.85810.1537.
- Viswanathan M, Kahwati L, Jahn B, Giger K, Dobrescu AI, Hill C, et al (2020). Universal screening for SARS‐CoV‐2 infection: a rapid review. Cochrane Database of Systematic Reviews(9). doi:10.1002/14651858.CD013718.
Appendix 3. Search strategy
Search sources.
Database: Ovid MEDLINE(R) and Epub Ahead of Print, In‐Process & Other Non‐Indexed Citations, Daily and Versions(R) 1946 to 12 November 2020
Date search conducted: 13 November 2020
1 exp Coronavirus/ (41876)
2 Coronavirus Infections/ (40179)
3 COVID‐19.rs. (35243)
4 severe acute respiratory syndrome coronavirus 2.os. (29910)
5 (2019 nCoV or 2019nCoV or 2019‐novel CoV).ti,ab,kf. (1285)
6 (Coronavir* or corona virus* or Middle East Respiratory Syndrome* or MERS or Severe Acute Respiratory Syndrome* or SARS*).ti,ab,kf. (60294)
7 COVID 19.mp. (68909)
8 (COVID19 or COVID 2019).ti,ab,kf. (1036)
9 (nCov 2019 or nCov 19).ti,ab,kf. (94)
10 or/1‐9 [Set 1: Coronaviruses] (97490)
11 Air Travel/ (396)
12 Travel/ (25483)
13 (border? adj3 (clos* or restrict* or control* or measure?)).ab,kf. (1139)
14 ((isolat* or quarantin*) adj6 (exposed or suspected or travel* or airport? or border?)).ti,ab,kf. (8173)
15 ((mobility or movement*) adj2 (reduc* or restrict*)).ti,ab,kf. (10041)
16 ((questionnaire* or RT‐PCR or screen* or surveil* or test* or telethermographic* or temperature or thermal imag* or thermal scan* or thermomet* or thermograph*) adj4 (traveller? or entr* or exit or border? or airport?)).ti,ab,kf. (5328)
17 (travel* or border?).ti. (26695)
18 (travel adj4 (measure? or intervention? or NPI?)).ab,kf. (463)
19 (travel* adj3 (restrict* or reduc* or control* or limit* or lockdown? or ban*)).ab,kf. (1806)
20 visa?.ti,ab,kf. (2175)
21 or/11‐20 [Set 2: Travel measures] (69713)
22 and/10,21 [Sets 1 & 2] (1700)
23 limit 22 to "humans only (removes records about animals)" (1675)
24 remove duplicates from 23 (1669)
Database: Ovid Embase 1996 to 2020 November 12
Date search conducted: 13 November 2020
1 coronaviridae/ (1048)
2 exp coronavirinae/ (21375)
3 exp coronavirus infection/ (22841)
4 (2019 nCoV or 2019nCoV or 2019‐novel CoV).ti,ab,kw. (1294)
5 (Coronavir* or corona virus* or Middle East Respiratory Syndrome* or MERS or Severe Acute Respiratory Syndrome* or SARS*).ti,ab,kw. (62133)
6 COVID 19.af. (65383)
7 (COVID19 or COVID 2019).ti,ab,kw. (1064)
8 (nCov 2019 or nCov 19).ti,ab,kw. (66)
9 or/1‐8 [Set 1: Coronaviruses] (103420)
10 air transportation/ (117)
11 aviation/ (7501)
12 travel/ (47727)
13 (border? adj3 (clos* or restrict* or control* or measure?)).ab,kw. (1329)
14 ((isolat* or quarantin*) adj6 (exposed or suspected or travel* or airport? or border?)).ti,ab,kw. (9807)
15 ((mobility or movement*) adj2 (reduc* or restrict*)).ti,ab,kw. (13481)
16 ((questionnaire* or RT‐PCR or screen* or surveil* or test* or telethermographic* or temperature or thermal imag* or thermal scan* or thermomet* or thermograph*) adj4 (traveller? or entr* or exit or border? or airport?)).ti,ab,kw. (6673)
17 (travel* or border?).ti. (29615)
18 (travel adj4 (measure? or intervention? or NPI?)).ab,kw. (553)
19 (travel* adj3 (restrict* or reduc* or control* or limit* or lockdown? or ban*)).ab,kw. (2244)
20 visa?.ti,ab,kw. (2493)
21 or/10‐20 [Set 2: Travel measures] (105738)
22 and/9,21 [Sets 1 & 2] (2420)
23 (animal experiment/ or exp animal/) not exp human/ (5031646)
24 22 not 23 (2389)
25 conference abstract.pt. (3904744)
26 24 not 25 (2248)
27 remove duplicates from 26 (2204)
Database: Cochrane COVID‐19 Study Register
URL: covid-19.cochrane.org (searched via the Cochrane Register of Studies: crsweb.cochrane.org)
Date search conducted: 13 November 2020
#1. (border* OR travel*):TI (102)
#2. (border* ADJ3 (clos* or restrict* or control* or measure*)):TI,AB (37)
#3. ((isolat* or quarantin*) AND (exposed or suspected or travel* or airport* or border*)):TI,AB (564)
#4. ((mobility or movement*) ADJ2 (reduc* or restrict*)):TI,AB (144)
#5. ((questionnaire* or RT‐PCR or screen* or surveil* or test* or telethermographic* or temperature or thermal imag* or thermal scan* or thermomet* or thermograph*) AND (traveller* or entr* or exit or border* or airport*)):TI,AB (266)
#6. (travel ADJ4 (measure* or intervention* or NPI*)):TI,AB (37)
#7. (travel* ADJ3 (restrict* or reduc* or control* or limit* or lockdown* or ban*)):TI,AB (162)
#8. visa*:TI,AB (1)
#9. #1 OR #2 OR #3 OR #4 OR #5 OR #6 OR #7 OR #8 (1064)
Database: WHO COVID‐19 Global literature on coronavirus disease
URL: search.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov
(ti:(border OR borders OR travel*)) OR (tw:(border* AND (clos* OR restrict* OR control* OR measure*))) OR (tw:((isolat* OR quarantin*) AND (exposed OR suspected OR travel* OR airport* OR border*))) OR (tw:((mobility OR movement*) AND (reduc* OR restrict*) AND travel*)) OR (tw:((questionnaire* or "RT‐PCR" or screen* or surveil* or test* or telethermographic* or temperature or "thermal image" or "thermal images" or "thermal imaging" or "thermal scan" or "thermal scans" or "thermal scanning" or thermomet* or thermograph*) AND (traveller* OR entr* OR exit OR border* OR airport*))) OR (tw:(travel AND (measure* OR intervention* OR NPI*))) OR (tw:(travel* AND (restrict* OR reduc* OR control* OR limit* OR lockdown* OR ban*))) OR (tw:(visa OR visas)) (5010)
Filters applied: WHO COVID, medRxiv, ELSEVIER, bioRxiv, LILACS, Grey literature, Lanzhou University/CNKI, ChemRxiv, WPRIM (Western Pacific), SSRN, ProQuest Central, PREPRINT‐SCIELO, PubMed, ArXiv (2287)
Appendix 4. Data extraction categories
Study information
- Study source (i.e. journal, report, preprint publication)
- Study type (e.g. experimental, quasi‐experimental, observational, modelling)
- Verbal summary of study type (e.g. stochastic discrete event simulation model)
Population, setting, and context
- Short description of population studied (e.g. international travelers including flight passenger and crew members arriving between 27 April and 22 June 2009)
Pathogen/disease
- Disease (i.e. SARS, MERS, COVID‐19)
- Stage of pandemic in the protected and restricted regions during which the intervention was implemented (i.e. no cases, sporadic cases, clusters of cases, and community transmission)
- Supporting information for the categorization of the stage of pandemic in the protected and restricted regions (e.g. date of notification of first case, current number of cases, established community spread)
- Reproduction number: basic reproduction number (R0) and current reproduction number R(t) for the region protected by the intervention
- Broad measure category (i.e. closure of national borders to entry and/or exit, international travel restrictions/bans, entry and/or exit screening at national borders, quarantine or isolation of travelers crossing national borders)
- Verbal summary of specific travel‐related measure(s) (e.g. all arriving passengers were provided with a symptom questionnaire)
- Mode of travel restricted by the travel‐related control measure(s)
- Any reported exceptions to the measure (e.g. certain individuals being excluded or fast‐tracked in airport screening because of nationality, occupation, country of origin)
- Representation of the intervention in the model (e.g. 100% travel reduction)
- Counterfactual/comparison
Primary outcomes (repeated for each primary outcome)
- Outcome category (i.e. cases avoided due to the intervention, number of cases detected due to the intervention, shift in epidemic development due to the intervention)
Secondary outcomes (repeated for each secondary outcome)
- Description of any secondary (non‐quantitative) outcomes (i.e. any other infectious disease transmission outcomes, healthcare utilisation outcomes, resource requirements for implementing the intervention, and any adverse effects)
Appendix 5. QUADAS‐2 domains as applied in the rapid review
Appendix 6. criteria used for assessing the quality of individual modelling studies, appendix 7. studies excluded from the analysis.
Twenty‐two studies were identified which met the inclusion criteria and assessed travel‐related control measures, but which did not contain sufficient data to inform decision‐making.
We identified eight observational studies assessing entry and/or exit screening measures and reporting only limited data on the effectiveness of the measures ( Chang 2020 ; Expert‐Taskforce 2020 ; Gupta 2020 ; Hayakawa 2020 ; Ing 2020 ; Jernigan 2020 ; Potdar 2020 ; Sriwijitalai 2020a ). Three studies described screening and management of travellers on cruise ships with data on the number of cases detected, number of individuals screening positive and proportion of deaths among the passengers and the crew ( Expert‐Taskforce 2020 ; Gupta 2020 ; Ing 2020 ). Another study also presented data on the number of cases detected due to entry symptom screening and testing measures in a cohort of individuals repatriated by air to Japan from China ( Hayakawa 2020 ). Four studies examined entry screening and quarantine measures for travellers crossing national borders. These studies reported data on travellers entering India ( Potdar 2020 ), Taiwan ( Chang 2020 ), Thailand ( Sriwijitalai 2020a ), and the USA ( Jernigan 2020 ); the studies reported on the number or proportion of cases detected, but did not provide data on how many cases were missed. In general, these studies showed that entry and/or exit screening measures and quarantine of travellers allows for the detection of cases in a variety of settings, however the effectiveness of the measures could not be further assessed with the reported data.
Nine observational ecological studies examined the effects of travel restrictions reducing or stopping cross‐border travel ( Arshed 2020 ; Chaudhry 2020 ; Jablonska 2020 ; Koh 2020 ; Leffler 2020 ; Liu 2020a ; Ogundokun 2020 ; Stokes 2020 ; Teixeira da Silva 2020 ). These studies provided aggregated data on the impact of the measures in this intervention category across various countries. Most of these studies reported aggregated data on more than 100 countries implementing the measures ( Arshed 2020 ; Koh 2020 ; Leffler 2020 ; Liu 2020a ; Stokes 2020 ; Teixeira da Silva 2020 ), and only one focused on Nigeria, specifically ( Ogundokun 2020 ). The regions restricted by the measures also varied and were often not reported specifically. The studies reported various outcomes related to the cases avoided due to the measure ( Chaudhry 2020 ; Leffler 2020 ; Ogundokun 2020 ; Stokes 2020 ; Teixeira da Silva 2020 ), and the shift in epidemic development ( Arshed 2020 ; Jablonska 2020 ; Koh 2020 ; Liu 2020a ). In general, the results from these studies reflect those of the main studies in this intervention category showing beneficial effects on the cases avoided and in shifting the epidemic development, with some suggestion that earlier implementation may be more effective.
Five modelling studies examined the effects of travel restrictions reducing or stopping cross‐border travel ( Baba 2020 ; Cacciapaglia 2020a ; Cacciapaglia 2020b ; Chen 2020d ; Jorritsma 2020 ). Four studies reported hypothetical regions for the measure implementation and restriction ( Baba 2020 ; Cacciapaglia 2020b ; Chen 2020d ; Jorritsma 2020 ), and one study simulated the measures in European countries ( Cacciapaglia 2020a ). One of the studies reported that reductions in travel into a region leads to a reduction in cumulative cases ( Chen 2020d ), another study reported on cases avoided due to the measure and found no beneficial effect ( Baba 2020 ), and three studies reported on the shift in epidemic development predicting delays in the outbreak, specifically when measures were implemented early ( Cacciapaglia 2020a ; Cacciapaglia 2020b ; Jorritsma 2020 ).
Appendix 8. QUADAS‐2 Risk of bias and applicability assessment of observational screening studies
Appendix 9. quality assessment of modelling studies, appendix 10. travel restrictions reducing or stopping cross‐border travel: study‐by‐study overview of the evidence contributing to each outcome (modelling studies), appendix 11. screening at borders: study‐by‐study overview of the evidence contributing to each outcome (modelling studies), appendix 12. study‐by‐study overview of the evidence contributing to each outcome for intervention categories 2 and 4 (observational studies), appendix 13. quarantine: study‐by‐study overview of the evidence contributing to each outcome (modelling studies), appendix 14. quarantine and screening at borders: study‐by‐study overview of the evidence contributing to each outcome (modelling studies).
These authors contributed equally to this work
New search for studies and content updated (no change to conclusions)
Characteristics of studies
Characteristics of included studies [ordered by study id].
CDC: Centers for Disease Control and Prevention; COI: conflicts of interest; COVID‐19: coronavirus disease 2019; EU: European Union; MERS: Middle East respiratory syndrome; NPI: non‐pharmaceutical intervention; n.r.: not reported; PCR: polymerase chain reaction; R(T): current reproduction number; RT‐PCR: reverse transcription polymerase chain reaction; SARS: severe acute respiratory syndrome; SEIR: susceptible, exposed, infectious, recovered; WHO: World Health Organization
Characteristics of excluded studies [ordered by study ID]
COVID‐19: coronavirus disease 2019; MERS: Middle East respiratory syndrome; SARS: severe acute respiratory syndrome
Differences between protocol and review
There are some differences between the protocol in Appendix 1 and some sections of the review.
Title: based on peer‐review feedback, we have revised the title for this review to add the word "international" to explicate the focus of the review. While this is in line with the scope of our protocol and review, we had not specified this in the title of the protocol.
Criteria for considering studies for this review: for this review, we added one post‐hoc exclusion criterion which we had not specified in the review protocol. This relates to studies of interventions assessing international travel but not concerned with cross‐border impacts, i.e. interventions to contain transmission within closed populations and only assessing their effect on those closed populations (e.g. on cruise ships or within detention centres). Furthermore, for this review update, we changed the eligibility criteria to only include studies focusing on SARS‐CoV‐2/COVID‐19 and exclude those focusing on SARS‐CoV‐1/SARS and MERS‐CoV/MERS. The latter, however, was considered in the first version of the review.
Secondary outcomes: for this review, we have added an additional secondary outcome, namely, user acceptability, based on exchanges with the WHO. This was not specified in the review protocol.
Data extraction and management: in the review protocol we specified that we would search for data from external sources, such as daily COVID‐19 situation reports published by the WHO, to enhance our understanding of the specific features of travel‐related control measures and the stage of pandemic at the time of implementing the measure. However, because of the lack of consistency of the information in these sources (e.g. discrepancies in how the WHO reports describe the stage of pandemic in earlier months of 2020), we did not search these sources in the review.
Assessment of risk of bias in included studies: in the review protocol we specified using multiple 'Risk of bias' tools to assess the broad range of study types in the review, namely RoB 2 tool for experimental studies, Risk of Bias in Non‐randomised Studies – of Interventions (ROBINS‐I) for quasi‐experimental and observational studies and a bespoke tool we developed for assessing the quality of modelling studies. However, given that we identified additional sets of papers evaluating entry and/or exit screening that were more related to diagnostic studies than intervention evaluation, we decided post‐protocol to apply an additional 'Risk of bias' tool, namely the Quality Assessment of Diagnostic Accuracy Studies (QUADAS‐2) to assess risk of bias of these studies. Furthermore, based on the rounds of feedback from the modelling experts in our team, we slightly adapted the criteria in the bespoke tool we developed for assessing the quality of modelling studies. These included criteria related to input data, internal and external validity. However, this did not change the overall structure of the tool.
Data synthesis: in our review we identified several studies which met the predefined inclusion criteria, which however did not provide directly policy‐relevant evidence. These included (i) observational studies evaluating entry and/or exit screening measures reporting only limited data regarding the effectiveness of the measure, (ii) observational ecological studies examining the aggregated impact of various travel‐related control measures across countries, and (iii) modelling studies using overly simplistic or theoretical assumptions and presenting abstract findings. We classified these studies as ‘supporting studies’ and summarised their results descriptively in an appendix. This process, however, was not prespecified in our review protocol.
Subgroup analyses and investigation of heterogeneity: we specified in our protocol that we would investigate the influence of several sources of heterogeneity on the impact of interventions, including the stage of the pandemic during which the intervention was implemented, whether the country is an island state or not, assumed infectious disease parameters, and whether a single or a package of control measures were implemented. While we reflect on the potential impact of some of these sources in our review, we did not explore heterogeneity as planned, because of lack of data on these sources.
Sensitivity analyses: in our review protocol we specified that we would conduct a sensitivity analysis to assess the impact of studies at high risk of bias on the review findings. However, we did not conduct sensitivity analysis in the review given the nature of our narrative synthesis and as most of the evidence base was comprised of modelling studies with major quality concerns.
Contributions of authors
JB, AM and ER developed the protocol, and JB and AM co‐ordinated the overall review process. JS had substantial intellectual contribution in the conduct of this review, including data extraction, risk of bias assessment and evidence synthesis. JB, AM, and EAR put together the report. All other review authors are listed in alphabetical order, with everyone making substantial intellectual contribution.
Protocol development: JB, AM and ER
Citation searches: AM, SV
Title and abstract screening: RB, MC, KG, CK, SK, JS, KS, BV, SV, KW
Full‐text screening: JB, RB, MC, KG, CK, SK, AM, JS, KS, BV, SV, KW
Data extraction: JB, KMFE‐F, CK, LMP, AM, SN, PvP, JMS, JS, BV, SV, KW
Risk of bias/quality assessment: JB, KMFE‐F, SH, ML, TL, JMS
GRADE assessment: JB, AM, ER
Evidence synthesis: JB, AM, ER, JMS
Manuscript preparation: JB, RB, MC, KMFE‐F, KG, SH, OH, CK, ML, TL, AM, LMP, PvP, ER, JMS, JS, KS, BV, SV, KW.
Sources of support
Internal sources.
- No sources of support supplied
External sources
Some of this article is based on a study commissioned and paid for by the WHO, who provided input which informed the review protocol and scope.
This review was undertaken in the context of COVID‐19 Evidence Ecosystem project, funded by the German Ministry of Education and Research.
Declarations of interest
Jacob Burns: the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the WHO for the conduct of this review. The WHO provided input which informed the review protocol and scope. The WHO did not exert any influence on how findings were interpreted. The role of the WHO is clearly specified in the review itself. The Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.
Ani Movsisyan: the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the WHO for the conduct of this review. The WHO provided input which informed the review protocol and scope. The WHO did not exert any influence on how findings were interpreted. The role of the WHO is clearly specified in the review itself. The Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.
J an M Stratil: the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the WHO for the conduct of this review. The WHO provided input which informed the review protocol and scope. The WHO did not exert any influence on how findings were interpreted. The role of the WHO is clearly specified in the review itself. The Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.
Renke L Biallas : the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the WHO for the conduct of this review. The WHO provided input which informed the review protocol and scope. The WHO did not exert any influence on how findings were interpreted. The role of the WHO is clearly specified in the review itself. The Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.
Michaela Coenen: the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the WHO for the conduct of this review. The WHO provided input which informed the review protocol and scope. The WHO did not exert any influence on how findings were interpreted. The role of the WHO is clearly specified in the review itself. The Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.
Karl Emmert‐Fees: none known
Karin Geffert: the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the WHO for the conduct of this review. The WHO provided input which informed the review protocol and scope. The WHO did not exert any influence on how findings were interpreted. The role of the WHO is clearly specified in the review itself. The Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.
Sabine Hoffman: none known
Olaf Horstick: none known
Michael Laxy: none known
Carmen Klinger : the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the WHO for the conduct of this review. The WHO provided input which informed the review protocol and scope. The WHO did not exert any influence on how findings were interpreted. The role of the WHO is clearly specified in the review itself. The Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.
Suzie Kratzer : the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the WHO for the conduct of this review. The WHO provided input which informed the review protocol and scope. The WHO did not exert any influence on how findings were interpreted. The role of the WHO is clearly specified in the review itself. The Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.
Tim Litwin : the Institute for Medical Biometry and Statistics (IMBI) at the University of Freiburg received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.
Susan Norris : the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the WHO for the conduct of this review. The WHO provided input which informed the review protocol and scope. The WHO did not exert any influence on how findings were interpreted. The role of the WHO is clearly specified in the review itself. The Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.
Lisa M Pfadenhauer: the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the WHO for the conduct of this review. The WHO provided input which informed the review protocol and scope. The WHO did not exert any influence on how findings were interpreted. The role of the WHO is clearly specified in the review itself. The Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.
Peter von Philipsborn: the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the WHO for the conduct of this review. The WHO provided input which informed the review protocol and scope. The WHO did not exert any influence on how findings were interpreted. The role of the WHO is clearly specified in the review itself. The Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.
Kerstin Sell: the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the WHO for the conduct of this review. The WHO provided input which informed the review protocol and scope. The WHO did not exert any influence on how findings were interpreted. The role of the WHO is clearly specified in the review itself. The Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.
Julia Stadelmaier: the Institute for Evidence in Medicine at the University of Freiburg received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.
Ben Verboom: the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the WHO for the conduct of this review. The WHO provided input which informed the review protocol and scope. The WHO did not exert any influence on how findings were interpreted. The role of the WHO is clearly specified in the review itself. The Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.
Stephan Voss: the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the WHO for the conduct of this review. The WHO provided input which informed the review protocol and scope. The WHO did not exert any influence on how findings were interpreted. The role of the WHO is clearly specified in the review itself. The Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.
Katherina Wabnitz : the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the WHO for the conduct of this review. The WHO provided input which informed the review protocol and scope. The WHO did not exert any influence on how findings were interpreted. The role of the WHO is clearly specified in the review itself. The Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.
Eva Rehfuess: the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the WHO for the conduct of this review. The WHO provided input which informed the review protocol and scope. The WHO did not exert any influence on how findings were interpreted. The role of the WHO is clearly specified in the review itself. The Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.
References to studies included in this review
Adekunle 2020 {published data only}.
- Adekunle A, Meehan M, Rojas-Alvarez D, Trauer J, McBryde E. Delaying the COVID-19 epidemic in Australia: evaluating the effectiveness of international travel bans . Australian and New Zealand Journal of Public Health 2020; 44 ( 4 ):257-9. [DOI: 10.1111/1753-6405.13016] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Al‐Qahtani 2020 {published data only}
- Al-Qahtani M, AlAli S, Abdul RAK, Salman AA, Otoom S, Atkin SL. The prevalence of asymptomatic and symptomatic COVID19 disease in a cohort of quarantined subjects . International Journal of Infectious Diseases 2020 Nov 3 [Epub ahead of print]. [ PMC free article ] [ PubMed ]
Al‐Tawfiq 2020 {published data only}
- Al-Tawfiq JA, Sattar A, Al-Khadra H, Al-Qahtani S, Al-Mulhim M, Al-Omoush O, et al. Incidence of COVID-19 among returning travelers in quarantine facilities: A longitudinal study and lessons learned . Travel Medicine and Infectious Disease 2020; 38 :101901. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Anderson 2020 {published data only}
- Anderson SC, Mulberry N, Edwards AM, Stockdale JE, Iyaniwura SA, Falcao RC, et al. How much leeway is there to relax COVID-19 control measures? medRxiv 2020. [DOI: 10.1101/2020.06.12.20129833] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Anzai 2020 {published data only}
- Anzai A, Kobayashi T, Linton NM, Kinoshita R, Hayashi K, Suzuki A, et al. Assessing the impact of reduced travel on exportation dynamics of novel coronavirus infection (COVID-19) . Journal of Clinical Medicine 2020; 9 ( 2 ):24. [DOI: ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Arima 2020 {published data only}
- Arima Y, Shimada T, Suzuki M, Suzuki T, Kobayashi Y, Tsuchihashi Y, et al. Severe acute respiratory syndrome coronavirus 2 infection among returnees to Japan from Wuhan, China, 2020 . Emerging Infectious Diseases 2020; 26 ( 7 ):1596-600. [DOI: 10.3201/eid2607.200994] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Nishiura H, Kobayashi T, Miyama T, Suzuki A, Jung SM, Hayashi K, et al. Estimation of the asymptomatic ratio of novel coronavirus infections (COVID-19) . International Journal of Infectious Diseases 2020; 94 :154-5. [DOI: 10.1016/j.ijid.2020.03.020] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Ashcroft 2020 {published data only}
- Ashcroft P, Lehtinen S, Angst DC, Low N, Bonhoeffer S. Quantifying the impact of quarantine duration on COVID-19 transmission . medRxiv 2020. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Banholzer 2020 {published data only}
- Banholzer N, Van Weenen E, Kratzwald B, Seeliger A, Tschernutter D, Bottrighi P, et al. Estimating the impact of non-pharmaceutical interventions on documented infections with COVID-19: a cross-country analysis . medRxiv 2020. [DOI: 10.1101/2020.04.16.20062141] [ CrossRef ] [ Google Scholar ]
Bays 2020 {published data only}
- Bays D, Bennett E, Finnie T. Investigating the potential benefit that requiring travellers to self-isolate on arrival may have upon the reducing of case importations during international outbreaks of influenza, SARS, Ebola virus disease and COVID-19 . medRxiv 2020. [ Google Scholar ]
- Bays D, Bennett E, Finnie T. Using simulation to assess the potential effectiveness of implementing screening at national borders during international outbreaks of influenza, SARS, Ebola virus disease and COVID-19 . medRxiv 2020. [ Google Scholar ]
Binny 2020 {published data only}
- Binny R, Baker MG, Hendy SC, James A, Lustig A, Plank MJ, et al. Early intervention is the key to success in COVID-19 control (preprint) . medRxiv 2020. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Boldog 2020 {published data only}
- Boldog P, Tekeli T, Vizi Z, Denes A, Bartha FA, Rost G. Risk assessment of novel coronavirus COVID-19 outbreaks outside China . Journal of Clinical Medicine 2020; 9 ( 2 ):19. [DOI: ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Chen J 2020 {published data only}
- Chen J, He H, Cheng W, Liu Y, Sun Z, Chai C, et al. Potential transmission of SARS-CoV-2 on a flight from Singapore to Hangzhou, China: An epidemiological investigation . Travel Medicine and Infectious Disease 2020; 36 :101816. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Chen T 2020 {published data only}
- Chen T, Huang S, Li G, Zhang Y, Li Y, Zhu J, et al. Quantitative effects of entry restrictions and travel quarantine on the next wave of COVID-19: Case studies of China and Singapore . SSRN 2020. [ Google Scholar ]
Chen Y‐H 2020 {published data only}
- Chen Y-H, Fang C-T. Combined interventions to suppress R0 and border quarantine to contain COVID-19 in Taiwan . Journal of the Formosan Medical Association 2020; 120 ( 2 ):903-5. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Chinazzi 2020 {published data only}
- Chinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak . Science 2020; 368 ( 6489 ):395-400. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Clifford 2020a {published data only}
- Clifford S, Pearson CA, Klepac P, Van Zandvoort K, Quilty BJ, Eggo RM, et al, CMMID COVID-19 Working Group. Effectiveness of interventions targeting air travellers for delaying local outbreaks of SARS-CoV-2 . Journal of Travel Medicine 2020; 8 :taaa068. [DOI: ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Clifford 2020b {published data only}
- Clifford S, Quilty BJ, Russell TW, Liu Y, Chan Yung-Wai D, Pearson CA, et al. Strategies to reduce the risk of SARS-CoV-2 re-introduction from international travellers . medRxiv 2020. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Costantino 2020 {published data only}
- Costantino V, Heslop DJ, MacIntyre CR. The effectiveness of full and partial travel bans against COVID-19 spread in Australia for travellers from China during and after the epidemic peak in China . Journal of Travel Medicine 2020 May 22 [Epub ahead of print]. [DOI: ] [ PMC free article ] [ PubMed ]
Davis 2020 {published data only}
- Davis JT, Chinazzi M, Perra N, Mu K, Piontti PY, Ajelli M, et al. Estimating the establishment of local transmission and the cryptic phase of the COVID-19 pandemic in the USA . medRxiv 2020. [ Google Scholar ]
Deeb 2020 {published data only}
- Deeb OE, Jalloul M. The dynamics of COVID-19 spread: evidence from Lebanon . Mathematical Biosciences and Engineering 2020; 17 ( 5 ):5618-32. [ PubMed ] [ Google Scholar ]
Dickens 2020 {published data only}
- Dickens BL, Koo JR, Lim JT, Sun H, Clapham HE, Wilder-Smith A, et al. Strategies at points of entry to reduce importation risk of COVID-19 cases and re-open travel . Journal of Travel Medicine 2020; 27 ( 8 ):taaa141. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Gostic 2020 {published data only}
- Gostic K, Gomez AC, Mummah RO, Kucharski AJ, Lloyd-Smith JO. Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19 . eLife 2020; 0 ( 02 ):24. [DOI: ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Grannell 2020 {published data only}
- Grannell JJ, Grannell JR. A two-region SEIR COVID-19 epidemic model for the island of Ireland . medRxiv 2020. [ Google Scholar ]
Hoehl 2020 {published data only}
- Hoehl S, Rabenau H, Berger A, Kortenbusch M, Cinatl J, Bojkova D, et al. Evidence of SARS-CoV-2 infection in returning travelers from Wuhan, China . New England Journal of Medicine 2020; 382 ( 13 ):1278-80. [DOI: ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
James 2020 {published data only}
- James A, Plank MJ, Hendy S, Binny H, Lustig A, Steyn N. Model-free estimation of COVID-19 transmission dynamics from a complete outbreak . medRxiv 2020. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Kang 2020 {published data only}
- Kang N, Kim B. The effects of border shutdowns on the spread of COVID-19 . Journal of Preventive Medicine and Public Health = Yebang Uihakhoe chi 2020; 53 ( 5 ):293-301. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Kim 2020 {published data only}
- Kim JG, Lee SH, Kim H, Oh HS, Lee J. Air evacuation of passengers with potential SARS-CoV-2 infection under the guidelines for appropriate infection control and prevention . Osong Public Health and Research Perspectives 2020; 11 ( 5 ):334-8. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Kivuti‐Bito 2020 {published data only}
- Kivuti-Bitok LW, Momodu AS, Cheptum JJ, Kimemia F, Gichuki I, Ngune I. System dynamics model of possible Covid-19 trajectories under various non-pharmaceutical intervention options in low-resource setting . medRxiv 2020. [ Google Scholar ]
Kwok 2020 {published data only}
- Kwok WC, Wong CK, Ma TF, Ho KW, Fan WTL, Chan KPF, et al. Border Restriction as a Public Health Measureto Limit Outbreak of Coronavirus Disease 2019 (COVID-19) . medRxiv 2020. [ Google Scholar ]
Lagier 2020 {published data only}
- Lagier JC, Colson P, Tissot Dupont H, Salomon J, Doudier B, Aubry C, et al. Testing the repatriated for SARS-Cov2: Should laboratory-based quarantine replace traditional quarantine? Travel Medicine and Infectious Disease 2020; 34 :101624. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Liebig 2020 {published data only}
- Liebig J, Najeebullah K, Jurdak R, Shoghri AE, Paini D. Should international borders re-open? The impact of travel restrictions on COVID-19 importation risk . medRxiv 2020. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Linka 2020a {published data only}
- Linka K, Peirlinck M, Kuhl E. The reproduction number of COVID-19 and its correlation with public health interventions . Computational Mechanics 2020; 66 ( 4 ):1035-50. [ PMC free article ] [ PubMed ] [ Google Scholar ]
- Linka K, Peirlinck M, Sahli Costabal F, Kuhl E. Outbreak dynamics of COVID-19 in Europe and the effect of travel restrictions . Computer Methods in Biomechanics & Biomedical Engineering, 1-8 2020; 23 ( 11 ):710-17. [DOI: 10.1080/10255842.2020.1759560] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Linka 2020b {published data only}
- Linka K, Rahman P, Goriely A, Kuhl E. Is it safe to lift COVID-19 travel bans? The Newfoundland story . Computational Mechanics 2020 Aug 29 [Epub ahead of print]. [ PMC free article ] [ PubMed ]
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Lio 2020 {published data only}
- Lio CF, Cheong HH, Lei CI, Lo IL, Yao L, Lam C, et al. The common personal behavior and preventive measures among 42 uninfected travelers from the Hubei province, China during COVID-19 outbreak: a cross-sectional survey in Macao SAR, China . PeerJ 2020; 8 :e9428. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Lytras 2020 {published data only}
- Lytras T, Dellis G, Flountzi A, Hatzianastasiou S, Nikolopoulou G, Tsekou K, et al. High prevalence of SARS-CoV-2 infection in repatriation flights to Greece from three European countries . Journal of Travel Medicine 2020; 27 ( 3 ):18. [DOI: ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Mandal 2020 {published data only}
- Mandal S, Bhatnagar T, Arinaminpathy N, Agarwal A, Chowdhury A, Murhekar M, et al. Prudent public health intervention strategies to control the coronavirus disease 2019 transmission in India: a mathematical model-based approach . Indian Journal of Medical Research 2020; 151 ( 2 & 3 ):190-9. [DOI: ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
McLure 2020 {published data only}
- McLure A, Lau LL, Furuya-Kanamori L. Has the effectiveness of Australia's travel bans against China on the importation of COVID-19 been overestimated? Journal of Travel Medicine 2020 Oct 10 [Epub ahead of print]. [ PMC free article ] [ PubMed ]
Nakamura 2020 {published data only}
- Nakamura H, Managi S. Airport risk of importation and exportation of the COVID-19 pandemic . Transport Policy 2020; 96 :40-7. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Ng 2020 {published data only}
- Ng OT, Marimuthu K, Chia PY, Koh V, Chiew CJ, De Wang L, et al. SARS-CoV-2 infection among travelers returning from Wuhan, China . New England Journal of Medicine 2020; 382 ( 14 ):1476-8. [DOI: ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Nowrasteh 2020 {published data only}
- Nowrasteh A, Forrester AC, Cato Institute. How US travel restrictions on China affected the spread of COVID-19 in the United States . www.cato.org 2020.
Nuckchady 2020 {published data only}
- Nuckchady D. Impact of public health interventions on the COVID-19 epidemic: a stochastic model based on data from an African island . medRxiv 2020. [ Google Scholar ]
Odendaal 2020 {published data only}
- Odendaal WG. A method to model outbreaks of new infectious diseases with pandemic potential such as COVID-19 . medRxiv 2020. [DOI: 10.1101/2020.03.11.20034512] [ CrossRef ] [ Google Scholar ]
Pinotti 2020 {published data only}
- Pinotti F, Di Domenico L, Ortega E, Mancastroppa M, Pullano G, Valdano E, et al. Tracing and analysis of 288 early SARS-CoV-2 infections outside China: A modeling study . PLoS medicine 2020; 17 ( 7 ):e1003193. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Quilty 2020 {published data only}
- Quilty BJ, Clifford S, Flasche S, Eggo RM, CCMID nCoV Working Group. Effectiveness of airport screening at detecting travellers infected with novel coronavirus (2019-nCoV) . Eurosurveillance 2020; 25 ( 5 ):2. [DOI: ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Russell TW 2020 {published data only}
- Russell TW, Wu JT, Clifford S, Edmunds WJ, Kucharski AJ, Jit M. Effect of internationally imported cases on internal spread of COVID-19: a mathematical modelling study . Lancet Public Health 2021; 6 ( 1 ):e12-20. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Russell WA 2020 {published data only}
- Russell WA, Buckeridge DL. Effectiveness of quarantine and testing to prevent COVID-19 transmission from arriving travelers . medRxiv 2020. [ Google Scholar ]
Ryu 2020 {published data only}
- Ryu S, Ali ST, Lim JS, Chun BC. Estimation of the excess COVID-19 cases in Seoul, South Korea by the students arriving from China . International Journal of Environmental Research and Public Health 2020; 17 :3113. [DOI: ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Shaikh Abdul Karim 2020 {published data only}
- Shaikh Abdul Karim S, Md Tahir FA, Mohamad UK, Abu Bakar M, Mohamad KN, Suleiman M, et al. Experience repatriation of citizens from epicentre using commercial flights during COVID-19 pandemic . International Journal of Emergency Medicine 2020; 13 ( 1 ):50. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Shi 2020 {published data only}
- Shi S, Tanaka S, Ueno R, Gilmour S, Tanoue Y, Kawashima T, et al. Travel restrictions and SARS-CoV-2 transmission: an effective distance approach to estimate impact . Bulletin of the World Health Organization 2020; 98 ( 8 ):518-29. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Sruthi 2020 {published data only}
- Sruthi CK, Biswal MR, Saraswat B, Joshi H, Prakash MK. How policies on restaurants, bars, nightclubs, masks, schools, and travel influenced Swiss COVID-19 reproduction ratios . medRxiv 2020. [ Google Scholar ]
Steyn 2020 {published data only}
- Steyn N, Plank MJ, James A, Binny RN, Hendy S, Lustig A. Managing the risk of a COVID-19 outbreak from border arrivals . medRxiv 2020. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Taylor 2020 {published data only}
- Taylor R, McCarthy CA, Patel V, Moir R, Kelly L, Snary E. The risk of introducing SARS-CoV-2 to the UK via international travel in August 2020 . medRxiv 2020. [ Google Scholar ]
Utsunomiya 2020 {published data only}
- Utsunomiya YT, Utsunomiya AT, Torrecilha RB, Paulan SC, Milanesi M, Garcia JF. Growth rate and acceleration analysis of the COVID-19 pandemic reveals the effect of public health measures in real time . Frontiers in Medicine 2020; 7 :247. [DOI: 10.3389/fmed.2020.00247] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Wells 2020 {published data only}
- Wells CR, Sah P, Moghadas SM, Pandey A, Shoukat A, Wang Y, et al. Impact of international travel and border control measures on the global spread of the novel 2019 coronavirus outbreak . Proceedings of the National Academy of Sciences of the United States of America 2020; 117 ( 13 ):7504-9. [DOI: ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Wilson 2020 {published data only}
- Wilson N, Baker MG, Eichner M. Estimating the impact of control measures to prevent outbreaks of COVID-19 associated with air travel into a COVID-19-free country: a simulation modelling study . medRxiv 2020. [DOI: 10.1101/2020.06.10.20127977] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Wong J 2020 {published data only}
- Wong J, Abdul Aziz ABZ, Chaw L, Mahamud A, Griffith MM, Lo YR, et al. High proportion of asymptomatic and presymptomatic COVID-19 infections in air passengers to Brunei . Journal of Travel Medicine 2020; 27 ( 5 ):taaa066. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Wong MC 2020 {published data only}
- Wong MC, Ng RW, Chong KC, Lai CK, Huang J, Chen Z, et al. Stringent containment measures without complete city lockdown to achieve low incidence and mortality across two waves of COVID-19 in Hong Kong . BMJ Global Health 2020; 5 ( 10 ). [ PMC free article ] [ PubMed ]
Yamahata 2020 {published data only}
- Tsuboi M, Hachiya M, Noda S, Iso H, Umeda T. Epidemiology and quarantine measures during COVID-19 outbreak on the cruise ship Diamond Princess docked at Yokohama, Japan in 2020: a descriptive analysis . Global Health & Medicine 2020; 2 ( 2 ):102-6. [DOI: 10.35772/ghm.2020.01037] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Yamahata Y, Shibata A. Preparation for quarantine on the cruise ship Diamond Princess in Japan due to COVID-19 . JMIR Public Health and Surveillance 2020; 6 ( 2 ):e18821. [DOI: ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Yang 2020 {published data only}
- Yang T, Liu Y, Deng W, Zhao W, Deng J. SARS-Cov-2 trajectory predictions and scenario simulations from a global perspective: a modelling study . Scientific Reports 2020; 10 ( 1 ):18319. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Zhang C 2020 {published data only}
- Zhang C, Qian LX, Hu JQ. COVID-19 pandemic with human mobility across countries . Journal of the Operations Research Society of China 2020 Aug 3 [Epub ahead of print].
Zhang L 2020 {published data only}
- Zhang L, Yang H, Wang K, Zhan Y, Bian L. Measuring imported case risk of COVID-19 from inbound international flights - A case study on China . Journal of Air Transport Management 2020; 89 :101918. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Zhong 2020 {published data only}
- Zhong L, Diagne M, Wang W, Gao J. Country distancing reveals the effectiveness of travel restrictions during COVID-19 . medRxiv 2020. [ Google Scholar ]
References to studies excluded from this review
Adiga 2020 {published data only}.
- Adiga A, Wang L, Sadilek A, Tendulkar A, Venkatramanan S, Vullikanti A, et al. Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics . medRxiv 2020. [DOI: ] [ Google Scholar ]
Aleta 2020 {published data only}
- Aleta A, Hu Q, Ye J, Ji P, Moreno Y. A data-driven assessment of early travel restrictions related to the spreading of the novel COVID-19 within mainland China . Chaos, Solitons & Fractals 2020; 139 :110068. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Annan 2015 {published data only}
- Annan A, Owusu M, Marfo KS, Larbi R, Sarpong FN, Adu-Sarkodie Y, et al. High prevalence of common respiratory viruses and no evidence of Middle East respiratory syndrome coronavirus in Hajj pilgrims returning to Ghana, 2013 . Tropical Medicine & International Health 2015; 20 ( 6 ):807-12. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Aravindakshan 2020 {published data only}
- Aravindakshan A, Boehnke J, Gholami E, Nayak A. Restarting after COVID-19: A data-driven evaluation of opening scenarios . medRxiv 2020. [DOI: 10.1101/2020.05.28.20115980] [ CrossRef ] [ Google Scholar ]
Arino 2020 {published data only}
- Arino J, Bajeux N, Portet S, Watmough J. Assessing the risk of COVID-19 importation and the effect of quarantine . medRxiv 2020. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Barkan 2020 {published data only}
- Barkan E, Shilo S, Talmor-Barkan Y. Comparison of SARS-CoV-2 exit strategies building blocks . medRxiv 2020. [DOI: 10.1101/2020.04.23.20072850] [ CrossRef ] [ Google Scholar ]
Batista 2020 {published data only}
- Batista B, Dickenson D, Gurski K, Kebe M, Rankin N. Minimizing disease spread on a quarantined cruise ship: A model of COVID-19 with asymptomatic infections . Mathematical Biosciences 2020; 329 :108442. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Bell 2004 {published data only}
- Bell DM, World Health Organization Working Group on International Community Transmission of SARS . Public health interventions and SARS spread, 2003 . Emerging Infectious Diseases 2004; 10 ( 11 ):1900-6. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Benkouiten 2013 {published data only}
- Benkouiten S, Charrel R, Belhouchat K, Drali T, Salez N, Nougairede A, et al. Circulation of respiratory viruses among pilgrims during the 2012 Hajj pilgrimage . Clinical Infectious Diseases 2013; 57 ( 7 ):992-1000. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Benkouiten 2014 {published data only}
- Benkouiten S, Charrel R, Belhouchat K, Drali T, Nougairede A, Salez N, et al. Respiratory viruses and bacteria among pilgrims during the 2013 Hajj . Emerging Infectious Diseases 2014; 20 ( 11 ):1821-7. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Benkouiten 2015 {published data only}
- Benkouiten S, Gautret P, Belhouchat K, Drali T, Nougairede A, Salez N, et al. Comparison of nasal swabs with throat swabs for the detection of respiratory viruses by real-time reverse transcriptase PCR in adult Hajj pilgrims . Journal of Infection 2015; 70 ( 2 ):207-10. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Borracci 2020 {published data only}
- Borracci RA, Giglio ND. Forecasting the effect of social distancing on COVID-19 autumn-winter outbreak in the metropolitan area of Buenos Aires . Estimacion del efecto del distanciamiento social sobre la epidemia de COVID-19 de otono-invierno en el area metropolitana de Buenos Aires. 2020; 80 Suppl 3 :7-15. [ PubMed ] [ Google Scholar ]
Branas 2020 {published data only}
- Branas CC, Rundle A, Pei S, Yang W, Carr BG, Sims S, et al. Flattening the curve before it flattens us: hospital critical care capacity limits and mortality from novel coronavirus (SARS-CoV2) cases in US counties . medRxiv 2020. [DOI: 10.1101/2020.04.01.20049759] [ CrossRef ] [ Google Scholar ]
Brauer 2008 {published data only}
- Brauer F, Van den Driessche P, Wang L. Oscillations in a patchy environment disease model . Mathematical Biosciences 2008; 215 ( 1 ):1-10. [ PubMed ] [ Google Scholar ]
Camitz 2006 {published data only}
- Camitz M, Liljeros F. The effect of travel restrictions on the spread of a moderately contagious disease . BMC Medicine 2006; 4 :32. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Channapathi 2020 {published data only}
- Channapathi T, Thatikonda S. Stochastic transmission dynamic model for evaluating effectiveness of control measures of COVID-19 . SSRN 2020. [DOI: ] [ Google Scholar ]
Cowling 2020 {published data only}
- Cowling BJ, Ali ST, Ng TW, Tsang TK, Li JC, Fong MW, et al. Impact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong: an observational study . Lancet Public Health 2020; 5 ( 5 ):e279-88. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Dandekar 2020 {published data only}
- Dandekar R, Barbastathis G. Quantifying the effect of quarantine control in COVID-19 infectious spread using machine learning . medRxiv 2020. [DOI: 10.1101/2020.04.03.20052084] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Daon 2020 {published data only}
- Daon Y, Thompson RN, Obolski U. Estimating COVID-19 outbreak risk through air travel . medRxiv 2020. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Dell'Omodarme 2005 {published data only}
- Dell'Omodarme M, Prati MC. The probability of failing in detecting an infectious disease at entry points into a country . Statistics in Medicine 2005; 24 ( 17 ):2669-79. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Dursun 2020 {published data only}
- Dursun ZB, Ulu-Kilic A, Alabay S, Benli AR, Celik I. COVID-19 among Turkish citizens returning from abroad . Travel Medicine and Infectious Disease 2020; 37 :101860. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Eksin 2020 {published data only}
- Eksin C, Ndeffo-Mbah M, Weitz JS. Reacting to outbreaks at neighboring localities . medRxiv 2020. [DOI: 10.1101/2020.04.24.20078808] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Erandi 2020 {published data only}
- Erandi KK, Mahasinghe AC, Perera SS, Jayasinghe S. Effectiveness of the strategies implemented in Sri Lanka for controlling the COVID-19 outbreak . Journal of Applied Mathematics 2020; 2020 :2954519. [ Google Scholar ]
Espinoza 2020 {published data only}
- Espinoza B, Castillo-Chavez C, Perrings C. Mobility restrictions for the control of epidemics: when do they work? SSRN 2020. [DOI: 10.2139/ssrn.3496928] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Fang 2020 {published data only}
- Fang Y, Nie Y, Penny M. Transmission dynamics of the COVID-19 outbreak and effectiveness of government interventions: a data-driven analysis . Journal of Medical Virology 2020. [DOI: 10.1002/jmv.25750] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Fouquet 2020 {published data only}
- Fouquet R, O'Garra T. The behavioural, welfare and environmental effects of air travel reductions during and beyond COVID-19 . SSRN 2020. [DOI: ] [ Google Scholar ]
Fredj 2020 {published data only}
- Fredj HB, Cherif F. Novel corona virus disease infection in Tunisia: mathematical model and the impact of the quarantine strategy . Chaos Solitons & Fractals 2020; 138 :109969. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Gardner 2016 {published data only}
- Gardner LM, Chughtai AA, MacIntyre CR. Risk of global spread of Middle East respiratory syndrome coronavirus (MERS-CoV) via the air transport network . Journal of Travel Medicine 2016; 23 ( 6 ):taw063. [DOI: 10.1093/jtm/taw063] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Gatto 2020 {published data only}
- Gatto M, Bertuzzo E, Mari L, Miccoli S, Carraro L, Casagrandi R, et al. Spread and dynamics of the COVID-19 epidemic in Italy: effects of emergency containment measures . Proceedings of the National Academy of Sciences of the United States of America 2020. [DOI: 10.1073/pnas.2004978117] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Gautret 2013a {published data only}
- Gautret P, Benkouiten S, Salaheddine I, Parola P, Brouqui P. Preventive measures against MERS-CoV for Hajj pilgrims . Lancet Infectious Diseases 2013; 13 ( 10 ):829-31. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Gautret 2013b {published data only}
- Gautret P, Charrel R, Belhouchat K, Drali T, Benkouiten S, Nougairede A, et al. Lack of nasal carriage of novel corona virus (HCoV-EMC) in French Hajj pilgrims returning from the Hajj 2012, despite a high rate of respiratory symptoms . Clinical Microbiology & Infection 2013; 19 ( 7 ):E315-7. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Gautret 2014 {published data only}
- Gautret P, Charrel R, Benkouiten S, Belhouchat K, Nougairede A, Drali T, et al. Lack of MERS coronavirus but prevalence of influenza virus in French pilgrims after 2013 Hajj . Emerging Infectious Diseases 2014; 20 ( 4 ):728-30. [ PMC free article ] [ PubMed ] [ Google Scholar ]
German 2015 {published data only}
- German M, Olsha R, Kristjanson E, Marchand-Austin A, Peci A, Winter AL, et al. Acute respiratory infections in travelers returning from MERS-CoV-affected areas . Emerging Infectious Diseases 2015; 21 ( 9 ):1654-6. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Gill 2020 {published data only}
- Gill BS, Jayaraj VJ, Singh S, Ghazali MS, Cheong YL, Md Iderus NH, et al. Modelling the effectiveness of epidemic control measures in preventing the transmission of COVID-19 in Malaysia . International Journal of Environmental Research and Public Health 2020; 17 ( 15 ):5509. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Godin 2021 {published data only}
- Godin A, Xia Y, Buckeridge DL, Mishra S, Douwes-Schultz D, Shen Y, et al. The role of case importation in explaining differences in early SARS-CoV-2 transmission dynamics in Canada - a mathematical modeling study of surveillance data . International Journal of Infectious Diseases 2021; 102 :254-9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Griffiths 2016 {published data only}
- Griffiths K, Charrel R, Lagier JC, Nougairede A, Simon F, Parola P, et al. Infections in symptomatic travelers returning from the Arabian peninsula to France: a retrospective cross-sectional study . Travel Medicine & Infectious Disease 2016; 14 ( 4 ):414-6. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Gunthe 2020 {published data only}
- Gunthe SS, Patra SS. Impact of international travel dynamics on domestic spread of 2019-nCoV in India: origin-based risk assessment in importation of infected travelers . Global Health 2020; 16 ( 1 ):45. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Hossain 2020 {published data only}
- Hossain MP, Junus A, Zhu X, Jia P, Wen TH, Pfeiffer D, et al. The effects of border control and quarantine measures on the spread of COVID-19 . Epidemics 2020; 32 :100397. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Hossein 2020 {published data only}
- Hossein RT, Shahriari S, Azad AK, Vafaee F. Real-time time-series modelling for prediction of COVID-19 spread and intervention assessment . medRxiv 2020. [DOI: 10.1101/2020.04.24.20078923] [ CrossRef ] [ Google Scholar ]
Huang 2020 {published data only}
- Huang L, Li L, Dunn L, He M. Taking account of asymptomatic infections in modeling the transmission potential of the COVID-19 outbreak on the Diamond Princess cruise ship . medRxiv 2020. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Hufnagel 2004 {published data only}
- Hufnagel L, Brockmann D, Geisel T. Forecast and control of epidemics in a globalized world . Proceedings of the National Academy of Sciences of the United States of America 2004; 101 ( 42 ):15124-9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Jia 2020 {published data only}
- Jia JS, Lu X, Yuan Y, Xu G, Jia J, Christakis NA. Population flow drives spatio-temporal distribution of COVID-19 in China . Nature 2020; 582 ( 7812 ):389-94. [ PubMed ] [ Google Scholar ]
Johansson 2011 {published data only}
- Johansson MA, Arana-Vizcarrondo N, Biggerstaff BJ, Staples JE, Gallagher N, Marano N. On the treatment of airline travelers in mathematical models . PLoS ONE 2011; 6 ( 7 ):e22151. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Joo 2019 {published data only}
- Joo H, Maskery BA, Berro AD, Rotz LD, Lee YK, Brown CM. Economic impact of the 2015 MERS outbreak on the Republic of Korea's tourism-related industries . Health Security 2019; 17 ( 2 ):100-8. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Jungerman 2017 {published data only}
- Jungerman MR, Vonnahme LA, Washburn F, Alvarado-Ramy F. Federal travel restrictions to prevent disease transmission in the United States: an analysis of requested travel restrictions . Travel Medicine & Infectious Disease 2017; 18 :30-5. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Kong 2020 {published data only}
- Kong XS, Liu F, Wang HB, Yang RF, Chen DB, Wang XX, et al. Epidemic prevention and control measures in China significantly curbed the epidemic of COVID-19 and influenza . medRxiv 2020. [DOI: 10.1101/2020.04.09.20058859] [ CrossRef ] [ Google Scholar ]
Kraemer 2020 {published data only}
- Kraemer MU, Yang CH, Gutierrez B, Wu CH, Klein B, Pigott DM, et al. The effect of human mobility and control measures on the COVID-19 epidemic in China . Science 2020; 368 ( 6490 ):493-7. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Krisztin 2020 {published data only}
- Krisztin T, Piribauer P, Woegerer M. The spatial econometrics of the coronavirus pandemic . Letters in Spatial and Resource Sciences 2020; 13 ( 3 ):209-18. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Lai CC 2020 {published data only}
- Lai CC, Hsu CY, Jen HH, Yen MF, Chan CC, Chen HH. Bayesian approach for modelling the dynamic of COVID-19 outbreak on the Diamond Princess Cruise Ship . medRxiv 2020. [ Google Scholar ]
Lai S 2020a {published data only}
- Lai S, Bogoch I, Ruktanonchai N, Watts A, Lu X, Yang W, et al. Assessing spread risk of Wuhan novel coronavirus within and beyond China, January-April 2020: a travel network-based modelling study . medRxiv 2020. [DOI: ] [ Google Scholar ]
Lai S 2020b {published data only}
- Lai S, Ruktanonchai NW, Carioli A, Ruktanonchai C, Floyd J, Prosper O, et al. Assessing the effect of global travel and contact reductions to mitigate the COVID-19 pandemic and resurgence . medRxiv 2020. [DOI: 10.1101/2020.06.17.20133843] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Lai S 2020c {published data only}
- Lai S, Ruktanonchai NW, Zhou L, Prosper O, Luo W, Floyd JR, et al. Effect of non-pharmaceutical interventions to contain COVID-19 in China . Nature 2020; 04 :04. [DOI: ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Lam 2020 {published data only}
- Lam HY, Lam TS, Wong CH, Lam WH, Leung CM, Au KW, et al. The epidemiology of COVID-19 cases and the successful containment strategy in Hong Kong - January to May 2020 . International Journal of Infectious Diseases 2020; 21 :21. [DOI: ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Lau 2004 {published data only}
- Lau JT, Yang X, Tsui HY, Pang E. SARS related preventive and risk behaviours practised by Hong Kong-mainland China cross border travellers during the outbreak of the SARS epidemic in Hong Kong . Journal of Epidemiology & Community Health 2004; 58 ( 12 ):988-96. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Lee 2020 {published data only}
- Lee VJ, Chiew CJ, Khong WX. Interrupting transmission of COVID-19: lessons from containment efforts in Singapore . Journal of Travel Medicine 2020; 27 ( 3 ):18. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Li H 2020 {published data only}
- Li H, Sun K, Persing DH, Tang YW, Shen D. Real-time screening of specimen pools for coronavirus disease 2019 (COVID-19) infection at Sanya Airport, Hainan Island, China . Clinical Infectious Diseases 2020 Sep 30 [Epub ahead of print]. [ PMC free article ] [ PubMed ]
Lin YC 2020 {published data only}
- Lin YC, Chen MY, Liu MC, Lin YJ, Lin Yu-H, Kuo JS, et al. Quarantine measures for coronavirus disease 2019 on a cruise ship, Taiwan, February 2020 . International Journal of Infectious Diseases 2020; 99 :298-300. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Lin YH 2020 {published data only}
- Lin YH, Huang JY, Yu KD, Lu CM, Lee WP, Huang JJ, et al. Border quarantine measures and achievement of COVID-19 control in Taiwan . Epidemiology Bulletin 2020; 36 ( 15 ):87-8. [ Google Scholar ]
Li R 2020 {published data only}
- Li R, Chen B, Zhang T, Ren Z, Song Y, Xiao Y, et al. Global COVID-19 pandemic demands joint interventions for the suppression of future waves . Proceedings of the National Academy of Sciences of the United States of America 2020; 117 ( 42 ):26151-7. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Liu 2011 {published data only}
- Liu X, Chen X, Takeuchi Y. Dynamics of an SIQS epidemic model with transport-related infection and exit-entry screenings . Journal of Theoretical Biology 2011; 285 ( 1 ):25-35. [ PubMed ] [ Google Scholar ]
Liu F 2020 {published data only}
- Liu F, Wang M, Zheng M. Effects of COVID-19 lockdown on global air quality and health . The Science of the Total Environment 2020; 755 ( Pt 1 ):142533. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Liu Q 2020 {published data only}
- Liu Q, Lu H, Chen R. Effect of a bundle of intervention strategies for the control of COVID-19 in Henan, a neighboring province of Wuhan, China . Wiener Klinische Wochenschrift 2020; 132 ( 13-14 ):396-399. [DOI: 10.1007/s00508-020-01688-9] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Luna 2007 {published data only}
- Luna LK, Panning M, Grywna K, Pfefferle S, Drosten C. Spectrum of viruses and atypical bacteria in intercontinental air travelers with symptoms of acute respiratory infection . Journal of Infectious Diseases 2007; 195 ( 5 ):675-9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Ma 2017 {published data only}
- Ma X, Liu F, Liu L, Zhang L, Lu M, Abudukadeer A, et al. No MERS-CoV but positive influenza viruses in returning Hajj pilgrims, China, 2013-2015 . BMC Infectious Diseases 2017; 17 :715. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Maeno 2016 {published data only}
- Maeno Y. Detecting a trend change in cross-border epidemic transmission . Physica A 2016; 457 :73-81. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Magalis 2020 {published data only}
- Magalis BR, Ramirez-Mata A, Zhukova A, Mavian C, Marini S, Lemoine F, et al. Differing impacts of global and regional responses on SARS-CoV-2 transmission cluster dynamics . bioRxiv 2020. [ Google Scholar ]
Malmberg 2020 {published data only}
- Malmberg H, Britton T. Inflow restrictions can prevent epidemics when contact tracing efforts are effective but have limited capacity . Journal of The Royal Society Interface 2020; 17 ( 170 ):20200351. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Marcelino 2012 {published data only}
- Marcelino J, Kaiser M. Critical paths in a metapopulation model of H1N1: efficiently delaying influenza spreading through flight cancellation . PLoS Currents 2012; 4 :e4f8c9a2e1fca8. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Myers 2020 {published data only}
- Myers JF, Snyder RE, Porse CC, Tecle S, Lowenthal P, Danforth ME, et al. Identification and monitoring of international travelers during the initial phase of an outbreak of COVID-19 - California, February 3-March 17, 2020 . MMWR - Morbidity & Mortality Weekly Report 2020; 69 ( 19 ):599-602. [ PubMed ] [ Google Scholar ]
Ng 2020a {published data only}
- Ng TCV, Cheng HY, Chang HH, Liu CC, Yang CC, Jian SW, et al. Effects of case- and population-based COVID-19 interventions in Taiwan . medRxiv 2020. [ Google Scholar ]
Nikolaou 2020 {published data only}
- Nikolaou P, Dimitriou L. Identification of critical airports for controlling global infectious disease outbreaks: stress-tests focusing in Europe . Journal of Air Transport Management 2020; 85 :101819. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Niwa 2020 {published data only}
- Niwa M, Hara Y, Sengoku S, Kodama K. Effectiveness of social measures against COVID-19 outbreaks in selected Japanese regions analyzed by system dynamic modeling . International Journal of Environmental Research and Public Health 2020; 17 ( 17 ):6238. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Pan 2020 {published data only}
- Pan W, Tyrovolas S, Vazquez IG, Raj R, Fernandez D, Zaitchik B, et al. COVID-19: Effectiveness of non-pharmaceutical interventions in the United States before phased removal of social distancing protections varies by region . medRxiv 2020. [ Google Scholar ]
Pitman 2005 {published data only}
- Pitman RJ, Cooper BS, Trotter CL, Gay NJ, Edmunds WJ. Entry screening for severe acute respiratory syndrome (SARS) or influenza: policy evaluation . BMJ 2005; 331 ( 7527 ):1242-3. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Pullano 2020 {published data only}
- Pullano G, Valdano E, Scarpa N, Rubrichi S, Colizza V. Population mobility reductions during COVID-19 epidemic in France under lockdown . medRxiv 2020. [DOI: 10.1101/2020.05.29.20097097] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Quilty 2020b {published data only}
- Quilty BJ, Diamond C, Liu Y, Gibbs H, Russell TW, Jarvis CI, et al. The effect of inter-city travel restrictions on geographical spread of COVID-19: evidence from Wuhan, China . medRxiv 2020. [DOI: 10.1101/2020.04.16.20067504] [ CrossRef ] [ Google Scholar ]
Rajabi 2020 {published data only}
- Rajabi A, Mantzaris AV, Mutlu EC, Garibay I. Investigating dynamics of COVID-19 spread and containment with agent-based modeling . medRxiv 2020. [ Google Scholar ]
Ruktanonchai 2020 {published data only}
- Ruktanonchai NW, Floyd JR, Lai S, Ruktanonchai CW, Sadilek A, Rente-Lourenco P, et al. Assessing the impact of coordinated COVID-19 exit strategies across Europe . Science 2020; 369 ( 6510 ):1465-70. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Ryu 2019 {published data only}
- Ryu S, Kim JJ, Cowling BJ, Kim C. Surveillance and public health response for travelers returning from MERS-CoV affected countries to Gyeonggi Province, Korea, 2016-2017 . Travel Medicine & Infectious Disease 2019; 31 :101350. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Shah 2020 {published data only}
- Shah NH, Sheoran N, Jayswal EN, Shukla D, Shukla N, Shukla J, et al. Modelling COVID-19 transmission in the United States through interstate and foreign travels and evaluating impact of governmental public health interventions . medRxiv 2020. [DOI: 10.1101/2020.05.23.20110999] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Shumway 2020 {published data only}
- Shumway B, Ibrahim D, Moss W. Monitoring returning travelers during the early weeks of the COVID-19 pandemic: one US county's experience . American Journal of Public Health 2020; 110 ( 7 ):962-3. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Sriwijitalai 2020b {published data only}
- Sriwijitalai W, Wiwanitkit V. Positive screening for Wuhan novel coronavirus infection at international airport: what's the final diagnosis for positive cases . International Journal of Preventive Medicine 2020; 11 :30. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Summan 2020 {published data only}
- Summan A, Nandi A. Timing of non-pharmaceutical interventions to mitigate COVID-19 transmission and their effects on mobility: a cross-country analysis . medRxiv 2020. [DOI: 10.1101/2020.05.09.20096420] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Sun 2013 {published data only}
- Sun G, Abe N, Sugiyama Y, Nguyen QV, Nozaki K, Nakayama Y, et al. Development of an infection screening system for entry inspection at airport quarantine stations using ear temperature, heart and respiration rates . Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine & Biology Society 2013; 2013 :6716-9. [ PubMed ] [ Google Scholar ]
Thomas 2014 {published data only}
- Thomas HL, Zhao H, Green HK, Boddington NL, Carvalho CF, Osman HK, et al. Enhanced MERS coronavirus surveillance of travelers from the Middle East to England . Emerging Infectious Diseases 2014; 20 ( 9 ):1562-4. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Valba 2020 {published data only}
- Valba O, Avetisov V, Gorsky A, Nechaev S. Self-isolation or borders closing: What prevents the spread of the epidemic better? Physical Review E 2020; 102 ( 1-1 ):010401. [ PubMed ] [ Google Scholar ]
Wells 2020b {published data only}
- Wells CR, Townsend JP, Pandey A, Krieger G, Singer BH, McDonald RH, et al. Optimal COVID-19 quarantine and testing strategies . medRxiv 2020. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Wickramaarachchi 2020 {published data only}
- Wickramaarachchi WP, Perera SS, Jayasinghe S. COVID-19 epidemic in Sri Lanka: a mathematical and computational modelling approach to control . Computational and Mathematical Methods in Medicine 2020; 2020 :4045064. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Yip 2007 {published data only}
- Yip PS, Hsieh YH, Xu Y, Lam KF, King CC, Chang HL. Assessment of intervention measures for the 2003 SARS epidemic in Taiwan by use of a back-projection method . Infection Control and Hospital Epidemiology 2007; 28 ( 5 ):525-30. [ PubMed ] [ Google Scholar ]
Yuan 2020 {published data only}
- Yuan HY, Hossain MP, Tsegaye MM, Zhu X, Jia P, Wen TH, et al. Estimating the risk on outbreak spreading of 2019-nCoV in China using transportation data . medRxiv 2020. [DOI: 10.1101/2020.02.01.20019984] [ CrossRef ] [ Google Scholar ]
Zhao Q 2020 {published data only}
- Zhao Q, Chen Y, Small DS. Analysis of the epidemic growth of the early 2019-nCoV outbreak using internationally confirmed cases . medRxiv 2020. [DOI: 10.1101/2020.02.06.20020941] [ CrossRef ] [ Google Scholar ]
Zhao Z 2020 {published data only}
- Zhao Z, Li X, Liu F, Zhu G, Ma C, Wang L. Prediction of the COVID-19 spread in African countries and implications for prevention and control: a case study in South Africa, Egypt, Algeria, Nigeria, Senegal and Kenya . Science of the Total Environment 2020; 729 :138959. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Zheng 2020 {published data only}
- Zheng Y. Estimation of disease transmission in multimodal transportation networks . Journal of Advanced Transportation 2020; 2020 . [DOI: 10.1155/2020/8898923] [ CrossRef ]
Additional references
Arshed 2020.
- Arshed N, Meo MS, Farooq F. Empirical assessment of government policies and flattening of the COVID19 curve . Journal of Public Affairs 2020:e2333. [ PMC free article ] [ PubMed ] [ Google Scholar ]
- Baba IA, Baba BA, Esmaili P. A mathematical model to study the effectiveness of some of the strategies adopted in curtailing the spread of COVID-19 . Computational and Mathematical Methods in Medicine 2020; 2020 :5248569. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Brozek 2021
- Brozek JL, Canelo-Aybar C, Akl EA, Bowen JM, Bucher J, Chiu WA, et al. GRADE guidelines 30: the GRADE approach to assessing the certainty of modeled evidence - An overview in the context of health decision-making . Journal of Clinical Epidemiology 2021; 129 :138-50. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Byambasuren 2020
- Byambasuren O, Cardona M, Bell K, Clark J, McLaws M-L, Glasziou P. Estimating the extent of asymptomatic COVID-19 and its potential for community transmission: systematic review and meta-analysis . medRxiv 2020. [DOI: 10.1101/2020.05.10.20097543] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Cacciapaglia 2020a
- Cacciapaglia G, Cot C, Sannino F. Second wave COVID-19 pandemics in Europe: a temporal playbook . Scientific Reports 2020; 10 ( 1 ):15514. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Cacciapaglia 2020b
- Cacciapaglia G, Sannino F. Interplay of social distancing and border restrictions for pandemics via the epidemic renormalisation group framework . Scientific Reports 2020; 10 ( 1 ):15828. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Campbell 2020
- Campbell M, McKenzie JE, Sowden A, Katikireddi SV, Brennan SE, Ellis S, et al. Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline . BMJ 2020; 368 :16890. [DOI: 10.1136/bmj.l6890] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Carfi A, Bernabei R, Landi F, for the Gemelli Against Covid-Post-Acute Care Study Group. Persistent symptoms in patients after acute COVID-19 . JAMA 2020; 324 ( 6 ):603-5. [ PMC free article ] [ PubMed ] [ Google Scholar ]
- Caro JJ, Eddy DM, Kan H, Kaltz C, Patel B, Eldessouki R, ISPOR-AMCP-NPC Modeling CER Task Forces. Questionnaire to assess relevance and credibility of modeling studies for informing health care decision making: an ISPOR-AMCP-NPC Good Practice Task Force report . Value in Health 2014; 17 ( 2 ):174-82. [DOI: 10.1016/j.jval.2014.01.003] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Chang K, Pan CY, Lu PL. Sentinel surveillance at airports: Experience of dengue and COVID-19 prevention in Taiwan . The Kaohsiung Journal of Medical Sciences 2020; 36 ( 8 ):665-6. [DOI: 10.1002/kjm2.12265] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Chaudhry 2020
- Chaudhry R, Dranitsaris G, Mubashir T, Bartoszko J, Riazi S. A country level analysis measuring the impact of government actions, country preparedness and socioeconomic factors on COVID-19 mortality and related health outcomes . EClinicalMedicine 2020; 25 :100464. [ PMC free article ] [ PubMed ] [ Google Scholar ]
- Chen M, Li M, Hao Y, Liu Z, Hu L, Wang L. The introduction of population migration to SEIAR for COVID-19 epidemic modeling with an efficient intervention strategy . Information Fusion 2020; 64 :252-8. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Dorjee 2020
- Dorjee K, Kim H, Bonomo E, Dolma R. Prevalence and predictors of death and severe disease in patients hospitalized due to COVID-19: A comprehensive systematic review and meta-analysis of 77 studies and 38,000 patients . PLoS One 2020; 15 ( 12 ):e0243191. [DOI: ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
- Egger M, Johnson L, Althaus C, Schoni A, Salanti G, Low N, et al. Developing WHO guidelines: time to formally include evidence from mathematical modelling studies . F1000Research 2017; 6 :1584. [DOI: 10.12688/f1000research.12367.2] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Errett 2020
- Errett NA, Sauer LM, Rutkow L. An integrative review of the limited evidence on international travel bans as an emerging infectious disease disaster control measure . Journal of Emergency Management 2020; 18 ( 1 ):7-14. [DOI: ] [ PubMed ] [ Google Scholar ]
Expert‐Taskforce 2020
- Expert-Taskforce. Epidemiology of COVID-19 Outbreak on Cruise Ship Quarantined at Yokohama, Japan, February 2020. Expert Taskforce for the Covid-Cruise Ship Outbreak . Emerging infectious diseases 2020; 26 ( 11 ):2591-7. [ PMC free article ] [ PubMed ] [ Google Scholar ]
- Fani M, Teimoori A, Ghafari S. Comparison of the COVID-2019 (SARS-CoV-2) pathogenesis with SARS-CoV and MERS-CoV infections . Future Virology 2020; 10 ( 2217 ):fvl-2020-0050. [DOI: 10.2217/fvl-2020-0050] [ CrossRef ] [ Google Scholar ]
Folayan 2015
- Folayan M, Brown B. Ebola and the limited effectiveness of travel restrictions . Disaster Medicine and Public Health Preparedness 2015; 9 ( 1 ):92. [DOI: 10.1017/dmp.2015.1] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Furukawa 2020
- Furukawa NW, Brooks JT, Sobel J. Evidence supporting transmission of severe acute respiratory syndrome coronavirus 2 while presymptomatic or asymptomatic . Emerging Infectious Diseases 2020; 26 ( 7 ). [DOI: 10.3201/eid2607.201595] [ PMC free article ] [ PubMed ] [ CrossRef ]
Garritty 2020
- Garritty C, Gartlehner G, Kamel C, King VJ, Nussbaumer-Streit B, Stevens A, et al. Cochrane rapid reviews. Interim guidance from the Cochrane Rapid Reviews Methods Group (2020) . methods.cochrane.org/rapidreviews/sites/methods.cochrane.org.rapidreviews/files/public/uploads/cochrane_rr_-_guidance-23mar2020-final.pdf .
Greenhalgh 2020
- Greenhalgh T, Knight M, Court C, Buxton M, Husain L. Management of post-acute covid-19 in primary care . BMJ 2020; 370 :m3026. [ PubMed ] [ Google Scholar ]
- Gupta A, Kunte R, Goyal N, Ray S, Singh K. A comparative analysis of control measures on-board ship against COVID-19 and similar novel viral respiratory disease outbreak: Quarantine ship or disembark suspects? Medical Journal Armed Forces India 2020 Jun 30 [Epub ahead of print]. [ PMC free article ] [ PubMed ]
Hayakawa 2020
- Hayakawa K, Kutsuna S, Kawamata T, Sugiki Y Nonaka C, Tanaka K, et al. SARS-CoV-2 infection among returnees on charter flights to Japan from Hubei, China: a report from National Center for Global Health and Medicine . Global Health & Medicine 2020:2020.01036. [ PMC free article ] [ PubMed ] [ Google Scholar ]
- US Department of Health & Human Services. What is the difference between isolation and quarantine? (2020) . Available at www.hhs.gov/answers/public-health-and-safety/what-is-the-difference-between-isolation-and-quarantine/index.html .
Higgins 2019
- Higgins JP, Savović J, Page MJ, Elbers RG, Sterne JA. Chapter 8: Assessing risk of bias in a randomized trial. In: Higgins JP, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al, editor(s). Cochrane Handbook for Systematic Reviews of Interventions version 6.0 (updated July 2019). Cochrane, 2019 . Available from www.training.cochrane.org/handbook .
Hilton Boon 2020
- Hilton Boon M, Thomson H. The effect direction plot revisited: Application of the 2019 Cochrane Handbook guidance on alternative synthesis methods . Research Synthesis Methods 2020; 12 ( 1 ):29-33. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Hultcrantz 2017
- Hultcrantz M, Rind D, Akl EA, Treweek S, Mustafa RA, Iorio A, et al. The GRADE Working Group clarifies the construct of certainty of evidence . Journal of Clinical Epidemiology 2017; 87 :4-13. [DOI: 10.1016/j.jclinepi.2017.05.006] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Ing AJ, Cocks C, Green JP. COVID-19: in the footsteps of Ernest Shackleton . Thorax 2020; 75 ( 8 ):693-4. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Jablonska 2020
- Jablonska K, Aballea S, Toumi M. Factors influencing the COVID-19 daily deaths peak across European countries . medRxiv 2020. [DOI: 10.1101/2020.11.04.20225656] [ PMC free article ] [ PubMed ] [ CrossRef ]
Jernigan 2020
- Jernigan DB. Update: public health response to the coronavirus disease 2019 outbreak - United States, February 24, 2020 . Morbidity and Mortality Weekly Report, Surveillance Summaries : MMWR 2020; 69 ( 8 ):216-9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Jorritsma 2020
- Jorritsma J, Hulshof T, Komjathy J. Not all interventions are equal for the height of the second peak . Chaos, Solitons, and Fractals 2020; 139 :109965. [ PMC free article ] [ PubMed ] [ Google Scholar ]
- Kiang MV, Chin ET, Huynh BQ, Chapman Lloyd AC, Rodriguez-Barraquer I, Greenhouse B, et al. Routine asymptomatic testing strategies for airline travel during the COVID-19 pandemic: a simulation analysis . medRxiv 2020:2020.12.08.20246132. [DOI: 10.1101/2020.12.08.20246132] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Koh WC, Naing L, Wong J. Estimating the impact of physical distancing measures in containing COVID-19: an empirical analysis . International Journal of Infectious Diseases 2020; 100 :42-9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Leffler 2020
- Leffler CT, Ing E, Lykins JD, Hogan MC, McKeown CA, Grzybowski A. Association of country-wide coronavirus mortality with demographics, testing, lockdowns, and public wearing of masks . American Journal of Tropical Medicine and Hygiene 2020; 103 ( 6 ):2153-4. [DOI: 10.4269/ajtmh.20-1342] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Liu Y, Morgenstern C, Kelly J, Lowe R, Jit M, CMMID COVID-19 Working Group. The impact of non-pharmaceutical interventions on SARS-CoV-2 transmission across 130 countries and territories . BMC Medicine 2021; 19 ( 1 ):40. [ PMC free article ] [ PubMed ]
- Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement . PLoS Medicine 2009; 6 ( 7 ):e1000097. [DOI: 10.1371/journal.pmed1000097] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Mouchtouri 2019
- Mouchtouri VA, Christoforidou EP, An der Heiden M, Menel Lemos C, Fanos M, Rexroth U, et al. Exit and entry screening practices for infectious diseases among travelers at points of entry: looking for evidence on public health impact . International Journal of Environmental Research & Public Health [Electronic Resource] 2019; 16 ( 23 ):21. [DOI: ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
Movsisyan 2021
- Movsisyan A, Burns J, Biallas R. Travel-related control measures to contain the COVID-19 pandemic: an evidence map . BMJ Open (in press). [ PMC free article ] [ PubMed ]
- National Institute for Health and Care Excellence. COVID-19 rapid guideline: managing the long-term effects of COVID-19. National Institute for Health and Care Excellence guideline [NG188] . 2020. [ PubMed ]
Nussbaumer‐Streit 2020
- Nussbaumer-Streit B, Mayr V, Dobrescu AI, Chapman A, Persad E, Klerings I, et al. Quarantine alone or in combination with other public health measures to control COVID-19: a rapid review . Cochrane Database of Systematic Reviews 2020, Issue 9 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
Nuttal 2014
- Nuttal I. Ebola travel: vigilance, not bans (commentary) World Health Organization . www.who.int/mediacentre/commentaries/ebola-travel/en/ (accessed prior to 22 March 2021).
- Nuzzo JB, Cicero AJ, Waldhorn R, Inglesby TV. Travel bans will increase the damage wrought by ebola . Biosecurity and Bioterrorism 2014; 12 ( 6 ):306-9. [DOI: 10.1089/bsp.2014.1030] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Ogundokun 2020
- Ogundokun RO, Lukman AF, Kibria GB, Awotunde JB, Aladeitan BB. Predictive modelling of COVID-19 confirmed cases in Nigeria . Infectious Disease Modelling 2020; 5 :543-8. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Ouzzani 2016
- Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews . Systematic Reviews 2016; 5 ( 1 ):210. [DOI: 10.1186/s13643-016-0384-4] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Philips 2006
- Philips Z, Bojke L, Sculpher M, Claxton K, Golder S. Good practice guidelines for decision-analytic modelling in health technology assessment: a review and consolidation of quality assessment . Pharmacoeconomics 2006; 24 ( 4 ):355-71. [DOI: 10.2165/00019053-200624040-00006] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Potdar 2020
- Potdar V, Choudhary ML, Bhardwaj S, Ghuge R, Sugunan AP, Gurav Y, et al. Respiratory virus detection among the overseas returnees during the early phase of COVID-19 pandemic in India . Indian Journal of Medical Research 2020; 151 ( 5 ):486-9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
- Rees EM, Nightingale ES, Jafari Y, Waterlow NR, Clifford S, Pearson CA, et al. COVID-19 length of hospital stay: a systematic review and data synthesis . BMC Medicine 2020; 18 ( 1 ). [ PMC free article ] [ PubMed ]
Schünemann 2008
- Schünemann HJ, Oxman AD, Brozek J, Glasziou P, Jaeschke R, Vist GE, GRADE Working Group. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies . BMJ 2008; 336 ( 7653 ):1106-10. [DOI: 10.1136/bmj.39500.677199.AE] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Schünemann 2019
- Schünemann HJ, Cuello C, Akl EA, Mustafa RA, Meerpohl JJ, Thayer K, GRADE Working Group. GRADE guidelines: 18. How ROBINS-I and other tools to assess risk of bias in nonrandomized studies should be used to rate the certainty of a body of evidence . Journal of Clinical Epidemiology 2019; 111 :105-14. [DOI: 10.1016/j.jclinepi.2018.01.012] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Sriwijitalai 2020a
- Sriwijitalai W, Wiwanitkit V. Positive screening for Wuhan novel coronavirus infection at international airport: what's the final diagnosis for positive cases . International Journal of Preventive Medicine 2020a; 11 :30. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Sterne 2016
- Sterne JA, Hernan MA, Reeves BC, Savovic J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions . BMJ 2016; 355 :i4919. [DOI: 10.1136/bmj.i4919] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Stokes 2020
- Stokes J, Turner AJ, Anselmi L, Morciano M, Hone T. The relative effects of non-pharmaceutical interventions on early Covid-19 mortality: natural experiment in 130 countries . medRxiv 2020. [DOI: 10.1101/2020.10.05.20206888] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Swadi T, Geoghegan J, Devine T, McElnay C, Sherwood J, Shoemack P, et al. Genomic evidence of in-flight transmission of SARS-CoV-2 despite predeparture testing . Emerging Infectious Diseases 2021; 27 ( 3 ):687-93. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Teixeira da Silva 2020
- Teixeira da Silva JA, Tsigaris P. Policy determinants of COVID-19 pandemic-induced fatality rates across nations . Public Health 2020; 187 :140-2. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Tognotti 2013
- Tognotti E. Lessons from the history of quarantine, from plague to influenza A . Emerging Infectious Diseases 2013; 19 ( 2 ):254-9. [DOI: 10.3201/eid1902.120312] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Viswanathan 2020
- Viswanathan M, Kahwati L, Jahn B, Giger K, Dobrescu AI, Hill C, et al. Universal screening for SARS-CoV-2 infection: a rapid review . Cochrane Database of Systematic Reviews 2020, Issue 9 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
Whiting 2011
- Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, QUADAS-2 Group. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies . Annals of Internal Medicine 2011; 155 ( 8 ):529-36. [DOI: 10.7326/0003-4819-155-8-201110180-00009] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- World Health Organization. International Health Regulations . 2nd edition. Geneva: World Health Organization, 2005. [ Google Scholar ]
- World Health Organization. Non-pharmaceutical public health measures for mitigating the risk and impact of epidemic and pandemic influenza . Geneva: World Health Organization, 2019. [ Google Scholar ]
- World Health Organization. Novel Coronavirus (2019-nCoV) Situation Report - 1 . Available from www.who.int/docs/default-source/coronaviruse/situation-reports/20200121-sitrep-1-2019-ncov.pdf?sfvrsn=20a99c10_4 2020.
- World Health Organization. Novel Coronavirus (2019-nCoV) Situation Report - 51 . Available from apps.who.int/iris/bitstream/handle/10665/331475/nCoVsitrep11Mar2020-eng.pdf?sequence=1&isAllowed=y 2020.
- Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention . JAMA 2020; 323 ( 13 ):1239-42. [DOI: 10.1001/jama.2020.2648] [ PubMed ] [ CrossRef ] [ Google Scholar ]
References to other published versions of this review
- Burns J, Movsisyan A, Stratil JM, Coenen M, Emmert-Fees KM, Geffert K, et al. Travel-related control measures to contain the COVID-19 pandemic: a rapid review . Cochrane Database of Systematic Reviews 2020, Issue 10 . Art. No: CD013717. [DOI: 10.1002/14651858.CD013717] [ PubMed ] [ CrossRef ] [ Google Scholar ]
International travel-related control measures to contain the COVID-19 pandemic: a rapid review
Affiliations.
- 1 Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany.
- 2 Pettenkofer School of Public Health, Munich, Germany.
- 3 Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, Munich, Germany.
- 4 Heidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany.
- 5 Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany.
- 6 Institute for Medical Biometry and Statistics (IMBI), Freiburg Center for Data Analysis and Modeling (FDM), Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- 7 Oregon Health & Science University, Portland, OR, USA.
- 8 Institute for Evidence in Medicine, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- PMID: 33763851
- PMCID: PMC8406796
- DOI: 10.1002/14651858.CD013717.pub2
Background: In late 2019, the first cases of coronavirus disease 2019 (COVID-19) were reported in Wuhan, China, followed by a worldwide spread. Numerous countries have implemented control measures related to international travel, including border closures, travel restrictions, screening at borders, and quarantine of travellers.
Objectives: To assess the effectiveness of international travel-related control measures during the COVID-19 pandemic on infectious disease transmission and screening-related outcomes.
Search methods: We searched MEDLINE, Embase and COVID-19-specific databases, including the Cochrane COVID-19 Study Register and the WHO Global Database on COVID-19 Research to 13 November 2020.
Selection criteria: We considered experimental, quasi-experimental, observational and modelling studies assessing the effects of travel-related control measures affecting human travel across international borders during the COVID-19 pandemic. In the original review, we also considered evidence on severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). In this version we decided to focus on COVID-19 evidence only. Primary outcome categories were (i) cases avoided, (ii) cases detected, and (iii) a shift in epidemic development. Secondary outcomes were other infectious disease transmission outcomes, healthcare utilisation, resource requirements and adverse effects if identified in studies assessing at least one primary outcome.
Data collection and analysis: Two review authors independently screened titles and abstracts and subsequently full texts. For studies included in the analysis, one review author extracted data and appraised the study. At least one additional review author checked for correctness of data. To assess the risk of bias and quality of included studies, we used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool for observational studies concerned with screening, and a bespoke tool for modelling studies. We synthesised findings narratively. One review author assessed the certainty of evidence with GRADE, and several review authors discussed these GRADE judgements.
Main results: Overall, we included 62 unique studies in the analysis; 49 were modelling studies and 13 were observational studies. Studies covered a variety of settings and levels of community transmission. Most studies compared travel-related control measures against a counterfactual scenario in which the measure was not implemented. However, some modelling studies described additional comparator scenarios, such as different levels of stringency of the measures (including relaxation of restrictions), or a combination of measures. Concerns with the quality of modelling studies related to potentially inappropriate assumptions about the structure and input parameters, and an inadequate assessment of model uncertainty. Concerns with risk of bias in observational studies related to the selection of travellers and the reference test, and unclear reporting of certain methodological aspects. Below we outline the results for each intervention category by illustrating the findings from selected outcomes. Travel restrictions reducing or stopping cross-border travel (31 modelling studies) The studies assessed cases avoided and shift in epidemic development. We found very low-certainty evidence for a reduction in COVID-19 cases in the community (13 studies) and cases exported or imported (9 studies). Most studies reported positive effects, with effect sizes varying widely; only a few studies showed no effect. There was very low-certainty evidence that cross-border travel controls can slow the spread of COVID-19. Most studies predicted positive effects, however, results from individual studies varied from a delay of less than one day to a delay of 85 days; very few studies predicted no effect of the measure. Screening at borders (13 modelling studies; 13 observational studies) Screening measures covered symptom/exposure-based screening or test-based screening (commonly specifying polymerase chain reaction (PCR) testing), or both, before departure or upon or within a few days of arrival. Studies assessed cases avoided, shift in epidemic development and cases detected. Studies generally predicted or observed some benefit from screening at borders, however these varied widely. For symptom/exposure-based screening, one modelling study reported that global implementation of screening measures would reduce the number of cases exported per day from another country by 82% (95% confidence interval (CI) 72% to 95%) (moderate-certainty evidence). Four modelling studies predicted delays in epidemic development, although there was wide variation in the results between the studies (very low-certainty evidence). Four modelling studies predicted that the proportion of cases detected would range from 1% to 53% (very low-certainty evidence). Nine observational studies observed the detected proportion to range from 0% to 100% (very low-certainty evidence), although all but one study observed this proportion to be less than 54%. For test-based screening, one modelling study provided very low-certainty evidence for the number of cases avoided. It reported that testing travellers reduced imported or exported cases as well as secondary cases. Five observational studies observed that the proportion of cases detected varied from 58% to 90% (very low-certainty evidence). Quarantine (12 modelling studies) The studies assessed cases avoided, shift in epidemic development and cases detected. All studies suggested some benefit of quarantine, however the magnitude of the effect ranged from small to large across the different outcomes (very low- to low-certainty evidence). Three modelling studies predicted that the reduction in the number of cases in the community ranged from 450 to over 64,000 fewer cases (very low-certainty evidence). The variation in effect was possibly related to the duration of quarantine and compliance. Quarantine and screening at borders (7 modelling studies; 4 observational studies) The studies assessed shift in epidemic development and cases detected. Most studies predicted positive effects for the combined measures with varying magnitudes (very low- to low-certainty evidence). Four observational studies observed that the proportion of cases detected for quarantine and screening at borders ranged from 68% to 92% (low-certainty evidence). The variation may depend on how the measures were combined, including the length of the quarantine period and days when the test was conducted in quarantine.
Authors' conclusions: With much of the evidence derived from modelling studies, notably for travel restrictions reducing or stopping cross-border travel and quarantine of travellers, there is a lack of 'real-world' evidence. The certainty of the evidence for most travel-related control measures and outcomes is very low and the true effects are likely to be substantially different from those reported here. Broadly, travel restrictions may limit the spread of disease across national borders. Symptom/exposure-based screening measures at borders on their own are likely not effective; PCR testing at borders as a screening measure likely detects more cases than symptom/exposure-based screening at borders, although if performed only upon arrival this will likely also miss a meaningful proportion of cases. Quarantine, based on a sufficiently long quarantine period and high compliance is likely to largely avoid further transmission from travellers. Combining quarantine with PCR testing at borders will likely improve effectiveness. Many studies suggest that effects depend on factors, such as levels of community transmission, travel volumes and duration, other public health measures in place, and the exact specification and timing of the measure. Future research should be better reported, employ a range of designs beyond modelling and assess potential benefits and harms of the travel-related control measures from a societal perspective.
Copyright © 2021 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.
Publication types
- Meta-Analysis
- Research Support, Non-U.S. Gov't
- Systematic Review
- COVID-19 / epidemiology
- COVID-19 / prevention & control*
- Communicable Diseases, Imported / epidemiology
- Communicable Diseases, Imported / prevention & control
- Internationality
- Models, Theoretical
- Observational Studies as Topic
- Pandemics / prevention & control*
- SARS-CoV-2*
- Travel-Related Illness*
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COMMENTS
If you have any questions or comments concerning this Travel Guide, please contact the Governor's Travel Control Board at (217) 782-2141, e-mail [email protected], or you may write to: Illinois Department of Central Management Services. State Travel Coordinator. Governor's Travel Control Board.
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International travel control measures are methods to manage international travel to contain the spread of COVID-19. Measures include: - closing international borders to stop travellers crossing from one country to another; - restricting travel to and from certain countries, particularly those with high infection levels;
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International travel control measures are methods to manage international travel to contain the spread of COVID-19. Measures include: closing international borders to stop travellers crossing from one country to another; restricting travel to and from certain countries, particularly those with high infection levels;
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U.S. citizens traveling to a country outside the U.S. Find country-specific travel advisories, including COVID-19 restrictions, from the Department of State. See the CDC's COVID-19 guidance for safer international travel to learn: If you can travel if you recently had COVID-19. What you can do to help prevent COVID-19. LAST UPDATED: May 31, 2024.
Verify that your room has proper locks and a secure safe for your valuables. Request a room located on higher floors for added security. Use door wedges or travel alarms to enhance the security of your room. Avoid sharing your room number or personal information with strangers. 3.
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Objectives: To comprehensively map the existing evidence assessing the impact of travel-related control measures for containment of the SARS-CoV-2/COVID-19 pandemic. Design: Rapid evidence map. Data sources: MEDLINE, Embase and Web of Science, and COVID-19 specific databases offered by the US Centers for Disease Control and Prevention and the ...
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The certainty of the evidence for most travel-related control measures is very low and the true effects may be substantially different from those reported here. Nevertheless, some travel-related control measures during the COVID-19 pandemic may have a positive impact on infectious disease outcomes. Broadly, travel restrictions may limit the ...
Page last reviewed: May 12, 2023. Content source: National Center for Emerging and Zoonotic Infectious Diseases (NCEZID) Division of Global Migration Health (DGMH) Advice for travelers before, during, and after their trips. Includes different types of travelers, reasons for travel, and tips for staying safe and healthy during travel.
International travel control measures are methods to manage international travel to contain the spread of COVID‐19. Measures include: ‐ closing international borders to stop travellers crossing from one country to another; ‐ restricting travel to and from certain countries, particularly those with high infection levels;
In December 2019, a novel coronavirus outbreak, COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China, as a cluster of cases exhibiting pneumonia-like symptoms ().Within a few weeks, the outbreak spread to 24 other countries around the world, including the United States, Canada, the United Kingdom, France, Australia, and Japan ().
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Abstract. Background: In late 2019, the first cases of coronavirus disease 2019 (COVID-19) were reported in Wuhan, China, followed by a worldwide spread. Numerous countries have implemented control measures related to international travel, including border closures, travel restrictions, screening at borders, and quarantine of travellers.
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