Number needed to isolate - a new population health metric to quantify transmission reductions from isolation interventions for infectious diseases
Bibliographic record
Abstract
BACKGROUND: We have previously developed and reported on a procedure for estimating the purported benefits of immunity mandates using a novel variant of the number needed to treat (NNT) which we called the number needed to isolate (NNI). Here we demonstrate its broader properties as a useful population health metric. MAIN BODY: The NNI is analogous to the number needed to treat (NNT = 1/ARR), except the absolute risk reduction (ARR) is the absolute transmission risk in a specific population. The NNI is the number of susceptible hosts in a population who need to be isolated to prevent one transmission event from them. The properties and utility of the NNI were modeled using simulated data and its model predictions were validated using real world data. The properties of the NNI are described for three categories of data from a previous study on transmissibility of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2): (1) in different settings, (2) after a specific exposure and (3) depending on symptomaticity status of susceptible hosts. CONCLUSIONS: We provide a demonstration of the utility of the NNI as a valuable population health metric to quantify the transmission reductions from isolation interventions.
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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 | 0.022 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".