Smartphone Trajectories as Data Sources for Agent‐Based Infection‐Spread Modeling
Bibliographic record
Abstract
The ubiquitous use of smartphones and other devices communicating over cellular networks and Bluetooth radios generates spatiotemporal usage data located with the cellular service provider and held within purpose-designed smartphone apps. These data can characterize a person's movements and interactions with high resolution accuracy, providing opportunities to apply these data as inputs to infectious disease modeling. In Canada, the penetration of cellular phones exceeds 80% nationally, making them a very strong proxy for people's movement and interaction patterns. This chapter overviews the types of cellular network data available from a telecommunications service provider and demonstrates their utility in estimating agent behavior patterns as suitable inputs into an agent-based model of contact-based infection spread. Two separate agent-based models of infection spread are summarized to illustrate the use of the data and a third is used to validate the data. As an extension, a Bluetooth-based smartphone application is presented, which allows one to detect others within one's proximity, extending the role a smartphone may play in modeling infection spread.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".