Development of an age-adjusted, activity-based contact probability model for infectious diseases
Bibliographic record
Abstract
The COVID-19 pandemic highlighted the importance of gathering restrictions in slowing the spread of communicable disease. Many restrictions on activities were applied without identifying how effective the restrictions might be in curtailing disease spread. We present a model that estimates the probability of contacting an infected individual as a function of prevalence and self-reported or hypothesized activities. The model incorporates an age adjustment factor to account for differences between the age demographics of infected versus activity participants. The age adjustment factor was important to include when the difference in prevalence between age groups was sufficiently large, and prevalence and activity group sizes were moderate. We applied our contact probability model to two scenarios to demonstrate how the model may inform the development of public health measures. Our model presents a method for estimating contact probability that could be adopted by jurisdictions considering facility closures or group size limits, or for individuals evaluating their own behaviours in future outbreaks or pandemics.
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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 | 0.003 |
| 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 | 0.000 |
| 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".