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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".