Dynamic resource allocation for epidemic control in multiple populations
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
We develop a dynamic resource allocation model in which a limited budget for epidemic control is allocated over multiple time periods to interventions that affect multiple populations. For certain special cases with two time periods, multiple independent populations, and a linear relationship between investment in a prevention programme and the resulting change in risky behaviour, we demonstrate that the optimal solution involves investing in each period as much as possible in some of the populations and nothing in all the other populations. We present heuristic algorithms for solving the general problem, and present numerical results. Our computational analyses suggest that good allocations can be made based on some fairly simple heuristics. Our analyses also suggest that allowing for some reallocation of resources over the time horizon of the problem, rather than allocating resources just once at the beginning of the time horizon, can lead to significant increases in health benefits. Allowing for reallocation of funds may generate more health benefits than use of a sophisticated model for one-time allocation of resources.
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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 | 0.002 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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