Bibliographic record
Abstract
The movement of people can influence the spread of diseases, especially in populated areas. While measures like quarantine can curb disease spread by restricting the movement of those infected, they come with socioeconomic consequences. Furthermore, not everyone might adhere to these restrictions, undermining their effectiveness. A more effective strategy is to educate people on the risks tied to their movement habits and recommend safer options. In this research, we introduce the problem of optimal risk-aware point-of-interest (POI) recommendations during epidemics, where people get recommendations on what POI to visit that reduces the risk of getting infected. The risk of infection at a POI is modeled based on its capacity and visit patterns over time. Then, we present a method that provides personalized recommendations which, when universally adopted, the overall risk is minimized. Unlike existing strategies, our method considers simultaneous user requests made in the same time period, which might influence the relative risk at POIs. An extensive evaluation was conducted, using real-world data coming from three major cities in Canada, which showed that our method outperforms the current state of practice method and other sensible baselines, on varying settings. Specifically, our method presented a decrease in the relative added risk of infection by 99.87%, 71.56% and 61.54% at each city, respectively. We also examined how the optimal solution is impacted if only a specific portion of the population follows the recommendation. Our optimal risk-aware recommendation method has the potential to reduce infection risk by promoting responsible behaviors within communities.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".