The effect of international travel on the spread of <scp>COVID</scp>‐19 in the United States
Bibliographic record
Abstract
Abstract We examine the relationship between incoming international passengers and COVID‐19 cases and deaths during the pandemic's initial wave in the United States. We find passengers from Milan, Italy, the location of an early outbreak, were an important source of exposure, increasing the early spread of COVID‐19 in the United States. Cities that received more passengers from Milan during the first quarter of 2020 experienced more COVID‐19 cases during March 2020 than cities receiving fewer passengers from Milan. Concurrently, cities that received more passengers from China or Rome (the latter not experiencing a major outbreak until later in 2020), did not experience increased cases. These results show passengers from at least one foreign epicenter were an important source of exposure that increased COVID‐19 spread in the United States. Given Milan was a secondary hotspot, our results also illustrate the importance of a holistic view of international pandemic hotspots when considering corresponding travel policy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".