Spatially Informed Back-Calculation for Spatio-Temporal Infectious Disease Models
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In epidemiological studies, the complete history of the disease system is seldom available; for example, we rarely observe the infection times of individuals but rather dates of diagnosis/disease reporting. The method of back-calculation together with prior knowledge about the distribution of the time from the infection to the disease reporting, called the incubation period, can be used to estimate unobserved infection times. Here, we consider the use of back-calculation in the context of spatial infectious disease models, extending the method to incorporate spatial information in the back-calculation method itself. Such a method should improve the quality of the fitted model, allowing us to better identify characteristics of the disease system of interest. We show that it is possible to better infer the underlying disease dynamics via the method of spatial back-calculation.
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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 | 0.001 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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