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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".