Estimating the risk reduction of isolation on COVID‐19 nonhousehold transmission and severe/critical illness in nonimmune individuals: September to November 2021
Bibliographic record
Abstract
There is growing scientific interest in immunity mandates/passports (IMP) for viral diseases in light of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. IMP isolate those who remain nonimmune from various settings to reduce nonhousehold transmissions from the nonimmune and reduce severe/critical illness among the nonimmune. A major limitation in the scientific literature is that there are currently no methods to quantify how many nonimmune individuals need to be isolated to achieve these purported benefits. This paper develops a procedure for estimating the benefits of IMP using a novel variant of the number needed to treat which we call the number needed to isolate (NNI). We use data from the SARS-CoV-2 pandemic to demonstrate the properties and utility of the NNI and to inform the debate about IMP. We focus on data from the European Union, United Kingdom, United States, Canada, Australia, and Israel during the fall 2021 when the Delta (B.1.617.2) variant predominated.
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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 | 0.029 | 0.106 |
| 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 | 0.001 | 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".