A non-homogeneous approach to simulating the spread of disease in a pandemic outbreak
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the event of a pandemic outbreak, emergency management units must coordinate an effective mitigation strategy to stop the disease spread using limited resources. In order to develop a successful response, it is necessary to have an accurate model of how the disease will spread. Previously presented models largely rely on homogeneous mixing models, which treat every member of the population as having identical infection risk. Intuitively, such an assumption is unrealistic. Certain demographic groups (e.g., healthcare workers, children and the elderly), have higher infection risks. Additionally, behavioral patterns such as use of public transportation impact infection risks. Using contact networks to represent the level of contact between population members and census data to approximate geographic location and travel patterns, we simulate the progression of a droplet-spread disease through the Greater Toronto Area. The results are periodically displayed on area maps using GIS software for visualization and planning purposes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.004 |
| 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 it