Modeling and simulating a disease outbreak by learning a contagion parameter-based model
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
Various advanced disease-surveillance models have been developed to provide early detection of infectious disease outbreaks and bioterrorist attacks. New methods that increase the overall detection capabilities of these systems can have a broad practical impact. This paper considers the problem of learning the Contagion Parameter (CP) in a black box model involving healthy, sick and contagious individuals. We base our study on a well-established model of contagion that is characterized by certain fixed parameters, some of which are known, while others are assumed unknown. In the modelling process, we assume that the individuals randomly move within a discretized grid, possibly infecting people or getting infected if they come in contact with healthy/sick individuals. In our study, the parameter of interest involves η which is the probability with which an infected person will transmit the disease to a healthy person. By invoking a novel learning strategy, we show how the CP can be computed using a Training and Testing phase. The results obtained by simulations are very impressive, and are pioneering to the best of our knowledge. The policy-related implications for the contagion control and disease outbreak are also open and very challenging.
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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 | 0.000 |
| 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