key: cord-1037195-3n0yursd authors: Hohlfeld, A.; abdullahi, l.; Abou-Setta, A. M.; Engel, M. E. title: International Travel-Related Control Measures to contain The Covid-19 Pandemic: An update to a Cochrane Rapid Review date: 2022-03-25 journal: nan DOI: 10.1101/2022.03.24.22271703 sha: a257f6cc217b993875b6a3004253a14b6ed1efb3 doc_id: 1037195 cord_uid: 3n0yursd Background: COVID-19 has proven to be more difficult to manage for many reasons including its high infectivity rate. One of the potential ways to limit its spread is by controlling free international travel. The objective of this systematic review is to identify, critically-appraise and summarize evidence on international travel-related control measures. Methods: This review is based on the Cochrane review: International travel-related control measures to contain the COVID-19 pandemic and followed the same methods. In brief, we searched for clinical and modelling studies in general health and COVID-19-specific bibliographic databases. The primary outcome categories were (i) cases avoided, (ii) a shift in epidemic development and, (iii) cases detected. 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. Results: We assessed 66 full-text articles that met with our inclusion criteria. Seventeen new studies (modelling = 9, observational = 8) were identified in the updated search. Most studies were of critical to moderate risk of bias. The added studies did not change the main conclusions of the Cochrane review nor the quality of the evidence (very low to low certainty). However, it did add to the evidence base for most outcomes. Conclusions: Weak evidence supports the use of international travel-related control measures to limit the spread of COVID-19 via air travel. Real-world studies are required to support these conclusions. The research question was defined using the PICO format. We employed similar methods as per Burns 2021 (1). We followed the same process of assessing risk of bias as set out by Burns 2021 (1) . Briefly, one reviewer rated the risk of bias, a second reviewer checked the judgements. We assessed risk of bias for observational studies using ROBINS-I (3) . There are no validated tools available to assess risk of bias of modelling studies; we used the bespoke assessment tool described previously (1, 4) . It was predicted that we would find observational studies and modelling studies. We separated these two studies designs and considered these as independent bodies of evidence (1) . Furthermore, regardless of study design, it was expected that we would find extensive heterogeneity across all studies' setting, population, intervention, and other contextual factors, as well as study methods. This meant that data would not be sufficiently similar to conduct meta-analyses. Therefore, we planned to descriptively synthesise the data in tables. We stratified studies by study design which were then further stratified according to interventions and outcomes. Further details on data synthesis that we followed are detailed in Burns 2021(1). Consistent with Burns 2021, a meta-analysis was not undertaken. The substantial heterogeneity of included studies would not allow for subgroup analysis. Nevertheless, we planned to ascertain and report the potential sources of heterogeneity that may influence intervention effectiveness. Further details can be found in Burns 2021(1). We summarised the included risk of bias for observational studies and quality of the modelling studies in Table 6 and Table 7 , respectively. We used ROBINS-I to assess the risk of bias of observational studies concerned with microbiological testing, quarantine, and both. Overall, the risk of bias is deemed to be critical and moderate across five and three studies, respectively. Table 6 depicts the bias due to confounding, which was critical in three studies, moderate in one, with no information available in four. Bias in selection of participants into the study was moderate in five studies, critical in one, while no information was provided in the two remaining studies. Bias in classification of interventions was critical in three studies, low and moderate in one each, with no information provided in three studies. Bias due to deviations from intended interventions was critical in two studies, moderate in two, low in one, with no information in two. Bias due to missing data was critical in two studies, moderate in four studies, low in one and no information in three. Bias in measurement of outcomes was critical in one study, moderate in three, low in one and no information in three studies. Bias in selection of the reported result was critical in one study, low in four studies and no information in three studies. Amongst the modelling studies, three had validation concerns while one other failed to justify the structural assumptions. Together with three new modelling studies identified in the updated search (10, 12, 13) , a total of four studies confirms that quarantine (with or without symptoms) and with no testing is effective in reducing the risk of transmission. Reduction in the proportion of imported cases ranged from 55% (7-days' quarantine) to 99% (21 days' quarantine) (very low certainty of evidence). Two modelling studies on microbiological detection (10, 14) were identified in the updated search. Assuming that exposure had occurred, pre-departure testing was predicted to improve the detection of cases by up to 84.4%, although results vary according to timing of the test. Two modelling studies (9, 10) demonstrated that a combination of quarantine and microbiological detection with a PCR test at day 7 significantly increases the proportion of cases averted as compared with quarantine alone, thus reducing the quarantine period. This outcome is subclassified into: a. Time to diagnosis b. Median time to outbreak c. Risk of an outbreak One new modelling study (8) on quarantine alone and in combination with microbiological detection assesses time to diagnosis. The results suggest that these interventions would decrease the mean interval of diagnosis from arrival to laboratoryconfirmation by up to 1.7 days. However, magnitude of effect may depend on testing sufficiency and lab capacity and policy implementation. One new modelling study (15) was identified in the updated search and reported that microbiological detection alone would delay an outbreak by 0.3 years. In combination with a 7-day quarantine or 21-day quarantine, the median time to outbreak respectively goes from to 0.6 years to virtual disappearance (very low-certainty evidence). One new modelling study (15) reported that pre-flight testing together with mask usage, symptom reporting and contact tracing reduced the risk of an outbreak from 88% to 37% (very low-certainty evidence). This outcome is subclassified into: a. Proportion of cases detected b. Reduction in local transmission c. Probability of releasing an infected individual into the community For this outcome, two observational studies on quarantine (19, 20) , were identified in the updated search, thus adding to the earlier modelling study (24) which indicated an increasing positivity correlating with increased quarantine. Amongst participants released from quarantine following a negative test result on arrival, positive cases constituted in excess of 30% in both observational studies (very low-certainty evidence). However, generalisability of these results may be impacted given that passengers in these studies were not stratified according to air vs travel, and the second study comprised military-related conditions. All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in Five observational studies assessing the impact of microbiological detection only on this outcome (17, 19, (21) (22) (23) reported detecting from 0.01% to 1.5% of cases amongst all travellers. Self-collection of samples was conducted at 4 days post-arrival (17) . Two observational studies assessed the impact of a combination of quarantine and microbiological detection on this outcome, reporting positivity rates of 0.2% and 0.6% following 10-14 days of quarantine amongst >25,000 PCR-negative arrivals to Bahrain (16) . The second study observed 26% positivity, with the majority detected within the first week (18) . Two modelling studies assessing the impact of isolation and quarantine on reducing local transmission (7, 11) were identified by the updated search. Implementing quarantine for all inbound travellers is the most efficient measure in reducing local cases by up to 23.3%. An even greater reduction (30 -35%) was observed through immediate isolation following symptom onset prior to, or during, travel while quarantine implemented following exposure without symptom monitoring or testing, leads to a risk reduction from 64 -95% (@7 days) to 96-100% (@14 days) A single study (11) reported that pre-departure testing can reduce risk of transmission during travel by between 10 -72% depending on time interval between testing and day of travel. Time of specimen collection, and degree of sensitivity of the test may introduce variability in the results. All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in Combination of interventions also had a single study (11) showing that on day 5-6: risk reduction = 97-100%. No new studies were identified in the updated search. The findings of the earlier review of three modelling studies (25) (9) (14) reported quarantine as having a positive effect on the probability of releasing an infected individual into the community. Steyn indicated that the probability of effect varies according to moderate and high transmission conditions. Combination of interventions had a single study (14) indicating that quarantine with testing of all incoming travellers would result in a risk of imported cases being released into the community of 25% following a 7-day quarantine period. We identified 17 studies which consisted modelling and observational studies in addition to the Cochrane review's 31 studies. These studies compared the effectiveness of travel-related control measures (specifically quarantine, microbiological detection or a combination thereof) as a means to limit the spread of COVID-19. These additional studies do not alter the findings, nor the quality of the evidence (very low certainty) as found in the Cochrane Review. Due to the nature of GRADEing the certainty of the evidence for modelling and the type observational studies, it is unlikely that more studies with these designs will change the quality of the evidence. Generally, the data, with very low certainty of evidence, indicate that these interventions are effective. However, there is a need for higher quality observational and experimental studies to support our findings. All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in MedRXiv and BioRXiv (n = 111) All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in All studies reported that quarantine (with or without symptoms) and with no testing is effective in reducing the risk of transmission. One study reported that 21-day quarantine had the highest efficiency at averting imported cases at 98.6% as compared with 14-day (91.8%) and 7-day (70.4%) periods; the remaining study demonstrated that quarantining passengers arriving from high-risk countries would result in a reduction in the proportion of imported cases ranging from 89% to 99%. The final study demonstrated the success of implementation of strict airport screening (no details available) and 14day self-quarantine protocols in reducing the risk of importation of COVID-19 in arrivals from 3 international regions. No vaccination data included in the report Very Low a,b Time to diagnosis 1 modelling study Chen Z, 2020 The study reported on the mean interval of diagnosis (in days) from arrival to laboratory-confirmation. Results indicate both a maximal increase by 1.7 days, to a maximal decrease by 0.11 days. No vaccination data included in the report These studies reported on isolation and quarantine. Chen demonstrated that implementing quarantine of all inbound travellers is the most efficient measure in preventing local transmission. Quarantine of symptomatic travellers only as compared with no quarantine results in a 5.1% and 23.3% reduction in local cases in China and Singapore, respectively. In the second study, immediate isolation following symptom onset prior to, or during, travel led to a 30 -35% reduction in risk of transmission during travel. Quarantine (alone) implemented following exposure without symptom monitoring or testing, leads to a risk reduction from 64 -95% (@7 days) to 96-100% (@14 days) No vaccination data included in the report Very Low j ⨁◯◯◯ Probability of releasing an infected individual into the community 1 modelling study Steyn 2020 The study reported that the probability of an infectious person, with no scheduled testing, leaving 14-day quarantine is 4% or 28%, under moderate and high transmission respectively. No vaccination data included in the report Very low c,d,f ⨁◯◯◯ a Downgraded -1 for indirectness, due to lack of reporting of external validation in some studies b RoB -1 for risk of bias due to 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 across some domains. c Downgraded -1 for imprecision, due to only one contributing study. d Downgraded -1 for imprecision, due to insufficient data reported to enable assessment of precision. e RoB -1 for risk of bias, due to quality concerns in the adequacy of assessment of the model's uncertainty. f RoB -1 for risk of bias due to quality concerns in the adequacy of assessment of the model's uncertainty, and incomplete technical documentation. g RoB -1 for risk of bias due to quality concerns in one or more domains h Downgraded -1 for imprecision, due to wide range of plausible effects i Downgraded -1 for indirectness, due to travellers not being representative of usual travellers j Downgraded -1 for risk of bias, due to uncertainty of temporal and geographical generalizability of data to all scenarios Assuming that exposure may have occurred at any time in the 7 days prior to departure, pre-departure testing can reduce risk of transmission during travel from 10%-72%, with reduction increasing as the interval between testing and day of travel decreases. The addition of entry testing to pre-departure testing improves the detection of cases imported from 57.2% (2-day pre-departure testing) to 84.8% (83.1% for rapid antigen pre-departure and on arrival). Results vary according to timing of pre-departure test. One study showed that testing within a 1-day interval between per-departure and arrival may give false negatives, with only 50% of infected travellers being detected. No vaccination data included in the report Very low a,b,c ⨁◯◯◯ Median time to outbreak 1 modelling study The study reported that pre-flight saliva testing (assuming a 62% sensitivity) will delay an outbreak by 0. 3 No vaccination data included in the report Risk of outbreak 1 modelling study The study reported the annual risk of an outbreak with pre-flight saliva testing alone is 88.4%. Preflight testing on days 1, 3 and 12 combined with mask usage until the last test, symptom reporting, and contact tracing further reduced the risk of an outbreak to 36 Across the studies, the proportion of cases amongst all travellers detected ranged from 0.01% to 1.5% over the periods of the study. One of the studies reported success at utilizing self-collection of samples conducted at 4 days post-arrival. Very Low e,f ⨁◯◯◯ Reduction in transmission 1 modelling study Johanssen 2020 Pre-departure testing can reduce risk of transmission during travel between 10 -72% depending on time interval between testing and day of travel. Variation in results is dependent on time of specimen collection, (with greatest effectiveness if taken on day of travel), and degree of sensitivity of the test, (with higher sensitivity test giving a higher reduction in risk). A single test reduces the risk of transmission after travel from 29-53%, regardless if performed at one or two days postarrival. No vaccination data included in the report Very Low d ⨁◯◯◯ a Downgraded -1 for indirectness, due to lack of reporting of external validation in some studies b Downgraded -1 for imprecision, due to insufficient data reported to enable assessment of precision. c RoB -1 for risk of bias due to quality concerns in the adequacy of assessment of the model's uncertainty, and incomplete technical documentation. d Downgraded -1 for imprecision, due to only one contributing study. e RoB -1 for risk of bias due to quality concerns in one or more domains f Downgraded -1 for indirectness, due to variation in testing methods One study ) demonstrated that a combination of quarantine with a day 7 PCR test/1 day delay in PCR results, gives a median reduction of 94% (UI 95%; 89%-98%) in infectious arriving-travellers being released into the community compared with no quarantine/no test scenario. For comparative outcomes with a no-testing scenario, a 14-day quarantine (median > 99% with 95% UI=98% to100%. A 5-day quarantine with a PCR-negative test results in a median reduction of 89% (UI, 83% -95%). Additionally, including pre-flight testing serves to reduce the duration of the quarantine period. The remaining study demonstrated that a PCR test on arrival increases the proportion of cases imported as compared with quarantine alone, especially for quarantined periods 7 days or less. The addition of a test upon exiting quarantine further increases the cases detected, especially at quarantine periods ≥ 5 days. Utilizing a pre-departure test together with PCR on arrival and rapid antigen testing when exiting quarantine showed the best performance overall with detection of cases equal to 91.2% (3 days quarantine) to 99.3% (21 days quarantine). Time to Diagnosis 1 modelling study Chen Z 2020 The study reported on the mean interval of diagnosis (in days) from arrival to laboratoryconfirmation. Results indicate both a maximal increase by 0.92 days, to a maximal decrease by 0.79 days. No vaccination data included in the report Very low a,c,d ⨁◯◯◯ One study reported delays in outbreak resulting from various lengths of quarantine stay combined with re-flight testing measures. These positive effects ranged from a median time to outbreak from 0.6 years delay [(95%, 8 days to 3.3 years); mean number of flights to outbreak = 322 flights] with a 7-day quarantine, to virtually zero risk of an outbreak with an extended 21 day quarantine period. No vaccination data included in the report Risk of outbreak 1 modelling study The study reported that pre-flight testing combined with 7-day quarantine has a risk of 67.8% of an annual outbreak; lengthening the quarantine period reduces the risk to 13 The two studies utilizing a combination of testing and quarantine as interventions, reported COVID positivity on arrivals ranging from 0.87 % to 1.2%. One study (Abdulrahman) reported a low proportion of cases (0.6%) amongst PCR-negative passengers quarantined in Bahrain. Fotheringham 2021 reported 88 (26.1%) positivity among 337 symptomatic individuals quarantined following the display of symptoms -63.6% (n = 56) were diagnosed as positive in the first week of quarantine. No data were provided from studies where passengers elected for self-quarantine. Very low e,f,g ⨁◯◯◯ 1 modelling study Johanssen 2020 On day 5-6: risk reduction = 97-100%. No vaccination data included in the report Very low a ⨁◯◯◯ Probability of releasing an infected 1 modelling study Steyn 2020 Quarantine with testing of all incoming travellers would result in a risk of imported cases being released into the community of 25% following a 7-day quarantine period. 1 -7% of infected travellers will be released into the community depending on level of transmission with quarantine facilities. Very low a,c,b ⨁◯◯◯ individual into the community No vaccination data included in the report a Downgraded -1 for imprecision, due to only one contributing study. b RoB -1 for risk of bias due to quality concerns in the adequacy of assessment of the model's uncertainty, and incomplete technical documentation. c Downgraded -1 for imprecision, due to insufficient data reported to enable assessment of precision. d RoB -1 for risk of bias due to quality concerns in the adequacy of assessment of the model's uncertainty. e RoB -1 for risk of bias due to quality concerns in one or more domains f Downgraded -1 for imprecision, due to a wide range of plausible effects. g Downgraded -1 for indirectness, due to not being representative of usual travel Limitations: • The results were derived based on the limited size of the sample and it only captured the early surge of the pandemic while concentrating on a few cities. • Evaluation only covered 17 major NPI strategies in response to imported COVID-19 risk control • Authors acknowledge Increases in mean interval may be attributed to insufficient testing and lab capacity in most of the cities in the early stages and that some cases may have quarantined more than once, which may also have prolonged the mean interval of confirmation. Chen T, 2021 (7) Study Characteristics Description: After creating a framework of methodology for quantifying the combined effects of entry restrictions and travel quarantine on managing the importation risk of COVID-19, the authors used a modified susceptibleexposed-infectious-recovered (SEIR) model to simulate the epidemic in mainland China and Singapore Limitations (acknowledged by authros): • Used data of overall global infection numbers rather than separated the calculation by country-level, for the purpose of simplifying the hypothetical scenarios. • Markov Chain Monte Carlo simulation conducted by sampling from empirical distributions of these parameters to adjust for parameter uncertainty, yet the distributions of the parameters which were retrieved from previous literature may not be temporally and geographically generalizable to all scenarios. • Third, simulation was based on international travel data in 2019, which may not represent the travel pattern of 2020. Due to the health threat of COVID-19, travel restrictions imposed by many countries and increased public awareness could reduce international travel. Clifford S, 2021 (9) Study Characteristics Mathematical modelling study • Description: statistical model to assess the effectiveness of quarantine and testing of international travellers to reduce risk of onward SARSCoV-2 transmission into a destination country in the pre-COVID-19 vaccination era. Travel control measure(s) • Quarantine zero days (low stringency) • Quarantine 3, 5, 7, or 9 days (moderate stringency) • Quarantine 14 (high/maximum stringency) Date of implementation: April to May 2020 Model parameters: Input • Simulated the number of infected air travellers intending to fly to a destination country in a given week based on the monthly volume of flights between the origin and destination, and considering the prevalence of COVID-19 in the origin country • Used the UK as a case study for the destination country • Assumed that the inbound and outbound travel is balanced on average • Time of each intending traveller's flight was sampled uniformly between the time of exposure to SARSCoV-2 and time of recovery • modelled international travellers coming either from the US or the EU, using publicly available Civil Aviation Authority data • Estimates of current COVID-19 infection prevalence were derived from reported cases and death time series data while adjusting for reporting delays and under-reporting based on case-fatality ratio estimates • EU-wide prevalence was calculated as a population-weighted mean of available country-level estimates of the non-UK EU countries (except Malta, for which a prevalence estimate was not available). • For each simulation, authors sampled the number of weekly intending travellers, the proportion of those who were infected, and the proportion of infected travellers who were symptomatic and asymptomatic • Quarantine period of 9 days with two tests and early exit may be able to largely replicate the impact of a 14-day quarantine period (RR: 0.02; 95% UI: 0.00-0.04) Test on arrival reduces the number of: PCR test on day 9 and release on day 10 reduces: • Symptomatic entering travellers by more than 99% (95% UI: 9-100) • Asymptomatic entering travellers by 92% (95% UI: 88-97) Effect of reducing the duration of quarantine • 9 days with either one or two rounds of testing may have a similar effect to that of the 14-day quarantine period (RR: 2.0; 95% UI: 1.00-infinity for a test on day 9 with release on day 10; RR: • 1.24; 95% UI: 0.53-infinity for a test on day 3 and day 9) Impact of pre-flight testing on the number of infectious travellers entering the community was greatest if implemented the day before departure (within 24 h) in scenarios with no post-flight testing, with an RR of 0.69 (95% UI: 0.64-0.73) compared with no testing either before departure or after. As quarantine increases in duration, the additional effect of pre-flight testing diminishes. Model assumptions • Syndromic screening is performed before departure, which may consist of thermal scanning and/or monitoring of symptoms such as cough and fever • Assumed in all scenarios that 70% of currently-symptomatic travellers do not fly (as modelled by Gostic et al. Limitations: • This study focused on the number of missed cases, excluding only those who completed their entire infection as not being missed. • Limited information available regarding test sensitivity in pre-symptomatic individuals and in those with particularly long incubation periods • Made assumptions on quarantine compliance in countries without institutional quarantine measures in place • Prevalence rates may vary across time (although dramatic changes across time of the study are unlikely to occur). • Heterogeneous prevalence rates may exist, however, within populations in countries according to social determinants and vaccine uptake, which were not accounted for. Reduction in transmission (before arrival) • Pre-departure testing at 3 days reduces risk during travel between 10-29%, while testing on the day of travel, reduces the risk further to 44-72% • Immediate isolation following symptom onset prior to or during travel: 30 -35% reduction in risk of transmission • 14-day quarantine: without symptom monitoring or testing: risk reduction = 96-100%; • 10-day quarantine: without symptom monitoring or testing: risk reduction = 84-100%; • 7-day quarantine: without symptom monitoring or testing: risk reduction = 64-95% • A single test on its own was most effective when performed 1-or 2-days post-arrival resulting in 29-53% and 29-51% reduction in transmission risk, respectively). • 7 day quarantine plus symptom monitoring plus test on day 5-6: risk reduction = 97-100%; Effect is similar for 10-day or 14-day quarantine. Model Quality Assessment: Model equation is provided and well-described. Model variables indicated in Model parameters: Input Country-specific epidemiological data on COVID-19 cases and international travel volume in South Korea from January to October 2020: • Data on the number of confirmed COVID-19 cases in South Korea were extracted from the Korea Disease Control and Prevention Agency (publicly available data). The epidemiological data included the dates of confirmation, dates of symptom onset, and transmission classification (local transmission/imported cases) [19] . • Data on the monthly number of passengers entering South Korea in 2019-2020 were gathered from Incheon International Airport (publicly available data) [20] . • Data on the number of confirmed cases of COVID-19 from the countries of origin were collected from the WHO situation report and countries' populations were obtained from Wikipedia This resulted in Risk of importation of COVID-19 in arrivals as follows: • Europe -reduction from 8.51% (March) to 0.59% (June). However, did increase from July onwards) • Asia -reduction from 2.25% (March) to 2.17% (April). However, did increase from May onwards • China -reduction from 0.02% (March) to 0.005% (October). • USA: No reduction Largely narrative descriptive discussion of results after the assumed implementation of control measures. "Therefore, multiple mitigation interventions (social distancing, a rapid diagnosis system, and movement restriction) should be implemented to reduce the spread of local and imported cases in South Korea." Model Quality Assessment: Equations were well described. Model variables indicated. Disease well described. Assumptions provided. Conducted a sensitivity analysis for Rt. Availability of technical documentation is unclear. Risk of importation was normalised by country using data on population, number of COVID-19 cases, and number of passengers from January to October 2020. Limitations: • This study relied on confirmed cases in South Korea -however, there were a substantial number of asymptomatic infections as 12.5% of serial intervals were negative -thus, secondary transmission caused by imported and local cases not considered • The risk was estimated by month as monthly passenger volume data were available. If daily data were given, the risk by country could be computed daily or weekly. Lunney M, 2021 (19) Study Characteristics Observational study • Description: Risk of COVID-19 importation and secondary transmission associated with a modified quarantine programme in Canada. Used bivariate logistic regression, then fitted a multivariable logistic regression model to estimate ORs for a positive test. Travel control measure(s) Programme requirements PCR COVID-19 test at the airport or Coutts/Sweetgrass land border crossing immediately on arrival in Canada • Negative arrival test result, participants leave quarantine with a second test 6 or 7 days after arrival. Participants who chose not to have a second test were required to complete a 14-day quarantine • Positive arrival test result, participants remain in quarantine for 14 days. Other requirements: • Completing a daily electronic report to monitor relevant symptoms associated with COVID-19 for the first 14 days following arrival in Canada • Daily report was required from each member of the household travel party, including children aged <5 years. • This evaluation reports the consequences of a modified quarantine programme. Rather than requiring international travellers to quarantine for 14 days after arrival, the programme released travellers from quarantine after receiving a negative COVID-19 test on arrival, with the requirement that they were tested again at day 6 or 7 and monitored their symptoms daily. • All PCR-positive individuals were diagnosed before being released into the general population of the base because of strict screening, quarantine, and exit criteria. Evidence for 14-day quarantine with testing (PCT or antibody followed by PCR) upon symptoms, is effective in preventing potentially positive cases from entering the local community. Recommend testing should be performed upon exit of quarantine, regardless of entry testing. If PCR is not available, serology testing should be done. If PCR is limited, positive serology testing should be followed by PCR. Requiring travellers to have a negative test result within 72 hours before travel could reduce resource requirements for public health services in the arriving location; however, combining this requirement with a post-arrival self-quarantine could be considered, because pretravel testing might be less effective than testing after arrival if used as a sole strategy. Limitations: • Test result data were derived from airport testing program briefs and could not be independently verified against laboratory results. • Handwritten travel declarations used before implementation of an electronic system resulted in some passengers checking multiple options or providing illegible information. • Test collection sites were outside of TSA secure areas; therefore, a small number of community members might have used airport testing when they had not traveled and were misclassified as travelers. • Participation in screening on arrival was not enforced and a small number of arriving travellers did not complete screening or select testing or self-quarantine. • The travel program did not include mechanisms for enforcement or for tracking of traveler point of origin, residency, or purpose of travel. In addition, the program relied on existing contact tracing systems for management of positive test results and did not include monitoring of road or seaports of entry. • Comprehensive data on post-arrival testing or on compliance with movement or activity restrictions were not collected, and data were not available on prospective travelers who changed pre-travel plans because of a positive pre-travel test. Pan 2020 ( Limitations in terms of the PICOs: • Pre-symptomatic period has not been included in the model development and assumption of parameter values are from early literature. • Model focused on import from the 7 countries with high levels of transmission at the time of analysis. Author recognize that the distribution of infections will change over time, shifting the risk of importation from countries beyond the selection. Retrospective study, although data sources appear to be reliable. Lack of clarity as regards the outcome of the 125,508 asymptomatic passengers and crew assigned to the special area of international aircraft docking. Limitations: Data of flights and passengers obtained from the information desk and website of PEK may not be verified. (24) The study does not provide a specific effect measure for the air travel-related control measure Bays 2021 (26) The study does not measure the specified interventions Chang 2020 (27) The study does not assess the impact of an air travel-related control measure Chen 2020 (28) The study does not measure the specified interventions Chen 2021 (29) The study does not provide a specific effect measure for an air travel-related control measure Cheng 2021 (30) The study does not assess the impact of an air travel-related control measure Chevaliera 2021 (31) The study does not provide a specific effect measure for the air travel-related control measure Chinyoka 2021 (32) The study does not measure the specified interventions Colavita 2021 (33) The study does not assess the impact of an air travel-related control measure Cowling 2020 (34) The study does not provide a specific effect measure for the air travel-related control measure Cruz-Pacheco 2020 (35) The study does not provide a specific effect measure for an air travel-related control measure Ghafari 2021 (36) The study does not measure the specified outcomes Goel 2021 (37) The study does not measure the specified outcomes Grout 2021 (38) The study does not include air travellers Haddaway 2021 The study does not assess the impact of an air travel-related control measure Hossain 2020 (39) The study does not assess the impact of an air travel-related control measure Hu 2021 (40) The study does not assess the impact of an air travel-related control measure Kamata 2021 (41) The study does not measure the specified interventions Kim 2021 (42) The study does not measure the specified interventions Kong 2021 (43) The study does not provide a specific effect measure for the air travel-related control measure Korzeniewski 2020 (44) The study does not provide a specific effect measure for an air travel-related control measure Kwok 2021 (45) The study does not include air travellers Lai 2021 (46) The study does not measure the specified interventions Liu 2021 (47) The study does not assess the impact of an air travel-related control measure Medby 2021 (48) The study did not incorporate the specified study design Meng 2021 (49) The study does not measure the specified interventions Movsisyan 2021 (50) The study does not provide a specific effect measure for an air travel-related control measure Murall 2021 (51) The study does not include air travellers All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in Norizuki 2021 (52) The study does not provide a specific effect measure for an air travel-related control measure Nussbaumer-Streit 2020 (53) The study does not provide a specific effect measure for an air travel-related control measure Pham 2021 (54) The study does not assess the impact of an air travel-related control measure Quach 2021 (55) The study does not measure the specified interventions Quilty 2020 (56) The study does not assess the impact of an air travel-related control measure Ramadan 2021 (57) The study did not incorporate the specified study design Reddy 2021 (58) The study does not assess the impact of an air travel-related control measure Smith 2021 (59) The study does not measure the specified interventions Song 2021 (60) The study does not assess the impact of an air travel-related control measure Thai 2021 (61) The study does not assess the impact of an air travel-related control measure University of British Columbia 2020 (62) Unable to access results The study does not provide a specific effect measure for an air travel-related control measure Well 2021 (63) The study does not measure the specified interventions Wells 2021 (64) The study does not assess the impact of an air travel-related control measure Yang 2021 (65) No data for analysis Yokota 2021 (66) The study did not incorporate the specified study design Yousif 2020 (67) The study does not assess the impact of an air travel-related control measure Yue 2021 (68) The study does not measure the specified interventions Zhou 2021 (69) The study does not assess the impact of an air travel-related control measure Zhu 2021 (70) The study does not measure the specified interventions Zhu 2021 (71) The study does not measure the specified interventions All rights reserved. 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