Modeling Pandemic Response for Populations Equipped with Contact-Chain Capable Wearable Devices
Bibliographic record
Abstract
Extant pandemic detection and prevention strategies are based around contact tracing technologies. In contrast, this manuscript describes a strategy for preemptive pandemic response based on wearable devices with built-in privacy, based on distributed, encrypted and anonymized contact chains. We evaluate such strategy in an agent-based simulation environment that models a wearable-device equipped population in an urban area, comparing it with no response strategy, and with the extant contact-tracing approaches. Our results suggest contact-chaining is an effective way to augment pandemic response, by preemptively isolating persons who are potentially infectious; initial results indicate up to a 23.4% reduction in peak infections compared to the strictest extant approach. Simulations show that this strategy is especially effective during a first wave and can potentially prevent further infection waves. Ongoing work is looking at the privacy issues of such an approach and modeling countermeasures within our simulation framework.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".