TESTING, TRACING AND SOCIAL DISTANCING: ASSESSING OPTIONS FOR THE CONTROL OF COVID_19
Bibliographic record
Abstract
Abstract In this work we present an analysis of non-pharmaceutical interventions implemented around the world in the fight against COVID-19: Social distancing, shelter-in-place, mask wearing, etc measures to protect the susceptible, together with, in various degrees, testing & contact-tracing to identify, isolate and treat the infected. The majority of countries have relied on the former, while ramping up their testing and tracing capabilities. We consider the examples of South Korea, Italy, Canada and the United States. By fitting a disease transmission model to daily case report data, we show that in each of the four countries their combination of social-distancing and testing/tracing to date have had a significant impact on the evolution of their pandemic curves. In this work we estimate the average isolation rates of infected individuals needing to occur in each country as a result of large-scale testing and contact tracing as a mean of lifting social distancing measures, without a resurgence of COVID-19. We find that an average isolation rate of an infected individual every 4.5 days (South Korea), 5.7 days (Canada) and to 6 days (Italy) would be sufficient. We also find that a rate of under 3.5 days will help in the United States, although it would not completely mitigate the second wave the country is currently under.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".