Do “soft” interventions matter more than vaccination? Rabies as an example
Bibliographic record
Abstract
Interventions such as vaccinations, treatment et cetera are usually the gold standard of disease control, as measured by reducing the reproduction number below unity. However, in practice, few diseases are reduced below this eradication threshold and instead persist despite active intervention campaigns. We propose an epidemic model of rabies with a saturated incidence rate that represents "soft" interventions such as public-awareness campaigns, animal curfews, fences etc. We prove local and global stability results based on the reproduction number. However, numerical simulations suggest that eradication is unlikely to occur using current practices. We thus investigate the effect of altering the saturated incidence term using "soft" interventions and show that near-eradication can be achieved even when the reproduction number exceeds unity. Soft interventions such as public-awareness campaigns, reducing contacts, animal curfews and fences can have a greater effect on eradicating rabies than current vaccination programs.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".