Optimal COVID-19 quarantine and testing strategies
Bibliographic record
Abstract
As economic woes of the COVID-19 pandemic deepen, strategies are being formulated to avoid the need for prolonged stay-at-home orders, while implementing risk-based quarantine, testing, contact tracing and surveillance protocols. Given limited resources and the significant economic, public health, and operational challenges of the current 14-day quarantine recommendation, it is vital to understand if shorter but equally effective quarantine and testing strategies can be deployed. To quantify the probability of post-quarantine transmission upon isolation of a positive test, we developed a mathematical model in which we varied quarantine duration and the timing of molecular tests for three scenarios of entry into quarantine. Specifically, we consider travel quarantine, quarantine of traced contacts with an unknown time if infection, and quarantine of cases with a known time of exposure. With a one-day delay between test and result, we found that testing on exit (or entry and exit) can reduce the duration of a 14-day quarantine by 50%, while testing on entry shortened quarantine by at most one day. Testing on exit more effectively reduces post-quarantine transmission than testing upon entry. Furthermore, we identified the optimal testing date within quarantines of varying duration, finding that testing on exit was most effective for quarantines lasting up to seven days. As a real-world validation of these principles, we analyzed the results of 4,040 SARS CoV-2 RT-PCR tests administered to offshore oil rig employees. Among the 47 positives obtained with a testing on entry and exit strategy, 16 cases that previously tested negative at entry were identified, with no further cases detected among employees following quarantine exit. Moreover, this strategy successfully prevented an expected nine offshore transmission events stemming from cases who had tested negative on the entry test, each one a serious concern for initiating rapid spread and a disabling outbreak in the close quarters of an offshore rig. This successful outcome highlights that appropriately timed testing can make shorter quarantines more effective, thereby minimizing economic impacts, disruptions to operational integrity, and COVID-related public health risks.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 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".