Assessing the risk of COVID-19 importation and the effect of quarantine
Bibliographic record
Abstract
Abstract Objectives During the early stage of COVID-19 spread, many governments and regional jurisdictions put in place travel restrictions and imposed quarantine after arrivals in an effort to slow down or stop the importation of cases. At the same time, they implemented non-pharmaceutical interventions (NPI) to curtail local spread. We assess the risk of importation of COVID-19 in locations that are at that point without infection or where local chains of transmission have extinguished, and evaluate the role of quarantine in this risk. Methods A stochastic SLIAR epidemic model is used. The effect of the rate, size, and nature of importations is studied and compared to that of NPI on the risk of importation-induced local transmission chains. The effect of quarantine on the rate of importations is assessed, as well as its efficacy as a function of its duration. Results The rate of importations plays a critical role in determining the risk that case importations lead to local transmission chains, more so than local transmission characteristics, i.e., strength of NPI. The latter influences the severity of the outbreaks. Quarantine after arrival in a location is an efficacious way to reduce the rate of importations. Conclusions Locations that see no or low level local transmission should ensure that the rate of importations remains low. A high level of compliance with post-arrival quarantine followed by testing achieves this objective with less of an impact than travel restrictions or bans.
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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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".