Estimating the effect of selective border relaxation on COVID-19 in New Zealand.
Bibliographic record
Abstract
AIMS: We developed a model, updated daily, to estimate undetected COVID-19 infections exiting quarantine following selectively opening New Zealand's borders to travellers from low-risk countries. METHODS: The prevalence of infectious COVID-19 cases by country was multiplied by expected monthly passenger volumes to predict the rate of arrivals. The rate of undetected infections entering the border following screening and quarantine was estimated. Level 1, Level 2 and Level 3 countries were defined as those with an active COVID-19 prevalence of up to 1/105, 10/105 and 100/105, respectively. RESULTS: With 65,272 travellers per month, the number of undetected COVID-19 infections exiting quarantine is 1 every 45, 15 and 31 months for Level 1, Level 2 and Level 3 countries, respectively. The overall rate of undetected active COVID-19 infections exiting quarantine is expected to increase from the current 0.40 to 0.50 per month, or an increase of one extra infection every 10 months. CONCLUSIONS: Loosening border restrictions results in a small increase in the rate of undetected COVID-19 infections exiting quarantine, which increases from the current baseline by one infection every 10 months. This information may be useful in guiding decision-making on selectively opening of borders in the COVID-19 era.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".