The economic impact of international travel measures used during the COVID-19 pandemic: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Assessment of the use of travel measures during COVID-19 has focused on their effectiveness in achieving public health objectives. However, the prolonged use of highly varied and frequently changing measures by governments, and their unintended consequences caused, has been controversial. This has led to a call for coordinated decision-making focused on risk-based approaches, which requires better understanding of the broader impacts of international travel measures (ITMs) on individuals and societies. METHODS: Our scoping review investigates the literature on the economic impact of COVID-19 ITMs. We searched health, social science and COVID-19-specific databases for empirical studies preprinted or published between 1 January 2020 and 31 October 2023. Evidence was charted using a narrative approach and included jurisdiction of study, ITMs studied, study design, outcome categories, and main findings. RESULTS: Twenty-six studies met the inclusion criteria and were included for data extraction. Twelve of them focused on the international travel restrictions implemented in early 2020. Limited attention was given to measures such as entry/exit screening and vaccination requirements. Eight studies focused on high-income countries, 6 on low-income and middle-income countries and 10 studies were comparative although did not select countries by income. Economic outcomes assessed included financial markets (n=13), economic growth (n=4), economic activities (n=1), performance of industries central to international travel (n=9), household-level economic status (n=3) and consumer behaviour (n=1). Empirical methods employed included linear regression (n=17), mathematical modelling (n=3) and mixed strategies (n=6). CONCLUSION: Existing studies have begun to provide evidence of the wide-ranging economic impacts resulting from ITMs. However, the small body of research combined with difficulties in isolating the effects of such measures and limitations in available data mean that it is challenging to draw general and robust conclusions. Future research using rigorous empirical methods and high-quality data is needed on this topic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".