Vaccine Prioritisation Using Bluetooth Exposure Notification Apps
Bibliographic record
Abstract
Abstract After vaccinating health care workers and vulnerable groups against COVID-19, authorities will need to decide how to vaccinate everyone else. Prioritising individuals with more contacts can be disproportionately effective, in theory, but identifying these individuals is difficult. Here we show that the technology underlying Bluetooth exposure notification applications, such as used for digital contact tracing, can be leveraged to prioritise vaccination based on individual contact data. Our approach is based on the insight that these apps also act as local sensing devices measuring each user's total exposure time to other users, thereby enabling the implementation of a previously impossible strategy that prioritises potential super-spreaders. Furthermore, by generalising percolation theory and introducing a novel measure of vaccination efficiency, we demonstrate that this ``hot-spotting" strategy can achieve herd immunity with up to half as many vaccines as a non-targeted strategy, and is attractive even for relatively low rates of app usage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".