Flunet: Automated tracking of contacts during flu season
Bibliographic record
Abstract
Abstract — By analyzing people’s contact patterns over time, it is possible to build efficient delay tolerant networking (DTN) algorithms and derive important data for parameterizing and calibrating epidemiological models. Significant research has been performed in the automated acquisition of contact patterns using mobile devices such as Zigbee motes or Bluetooth-enabled cellular phones. However, the limited number of studies described to date do not capture the breadth of human experience or specifically include the acquisition of health related information. In this paper we present Flunet, a mobile contact-tracking network deployed in a Canadian university environment during flu season. Flunet tracked contact patterns of 36 participants and their proximity to 11 stationary nodes using MicaZ motes over a period of three months. Participants filled out weekly surveys on their state of health. This study is distinct from others because we incorporate health information and the impact of sub-zero temperatures on mobility patterns. This paper presents a preliminary analysis of the data set, primarily from a DTN perspective. We present fundamental attributes of the dataset, the efficiency of routing for single pass and flooding-based algorithms and a preliminary look at the relationship between network characteristics and health status.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".