A Mobility-based Recommendation System for Mitigating the Risk of Infection during Epidemics
Bibliographic record
Abstract
The relationship between human mobility and the spread of an infectious disease has been well documented. At the same time, availability of mobility data is growing due to advancements in digital contact tracing mobile applications and GPS-enabled devices. Motivated by these observations, we have designed and developed STRIPE (Safe Trips during Epidemics), a mobility-based recommendation system that can provide safer trip recommendations to individuals. The recommendation model considers the risk of infection of alternative trips between an origin and destination. It also considers the risk of infection of specific points of interests (POIs) that occur at the microscale. In this paper, we present a high-level architecture of the system, its main features and system use cases. The broader impact of our research is that by helping individuals making informed decisions, we promote more responsible behaviors in the community as a whole that could effectively alleviate the impact of the epidemic.
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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 | 0.005 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".