2022년 국립인천공항검역소 의뢰 해외입국자 코로나바이러스감염증-19 검사 현황
Bibliographic record
Abstract
We analyzed the COVID-19 test results of overseas entrants through Incheon International Airport in 2022. The total number of tests was 21,234, of which 6,149 were confirmed positive, and the positive rate was 29.0%, a significant increase from 5.3% in 2021. The increased test number, due to the prevalence of Omicron, was lasted until March 2022 and gradually decreased until June. The positive rate was also decreased to 5.6% in June. After that, the test number and positive rate began to increase again, and the positive rate was maintained from 34.9% to 59.3% until December. The COVID-19 tests were conducted on overseas entrants from 128 countries. The order of the top 15 countries was Unites States, Vietnam, Japan, etc. The number of tests in the top 15 countries was 16,498, which was 77.7% of the total number of tests. In the first half of the year, there were many tests conducted in the United States, Japan, Canada, etc., and in the second half, increased number of tests was confirmed in Philippines, Thailand, Vietnam, etc. The positive rates for each country showed a similar trend to the overall positive rate changes in most country including the 15 countries. This trend is presumed to be the result of mitigation of quarantine measures by stabilizing of the outbreak of COVID-19 due to Omicron and vaccination. As such, we will maintain a rapid and accurate laboratory inspection system to prevent and control infectious diseases derived from abroad such as COVID-19 at quarantine stage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".