No new community COVID-19 infection in four consecutive weeks: what lesson can be learned from Vietnam
Bibliographic record
Abstract
Sharing a common land border with China, Vietnam has faced a high risk of transmission of Coronavirus Disease 2019 (COVID-19). Rapid decision making and robust public health measures were established by the Vietnamese Government to control the situation. As of 17 May 2020, Vietnam reported 320 total confirmed cases of COVID-19, of whom 260 had fully recovered, while the remaining 60 cases were still under treatment. Noteworthy, the current data still confirms zero deaths and within the last 32 consecutive days prior to this submission, there have been no new infections in the country. Valuable lessons from Severe Acute Respiratory Syndrome in 2003 such as use of quarantine, early recognition and quick response to the infection, and increased awareness of its citizens have put Vietnam in a somewhat better position against COVID-19 compared to other places. Vietnam, at the current time, mulls declaring an end of the current COVID-19 outbreak.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".