Reassessing global historical ℛ <sub>0</sub> estimates of canine rabies
Bibliographic record
Abstract
Abstract Rabies spread by domestic dogs continues to cause tens of thousands of human deaths every year in low- and middle-income countries. Nevertheless rabies is often neglected, perhaps because it has already been eliminated from high-income countries through dog vaccination. Estimates of canine rabies’s intrinsic reproductive number ( ℛ 0 ), a metric of disease spread, from a wide range of times and locations are relatively low (values < 2), with narrow confidence intervals. Given rabies’s persistence, this consistently low and narrow range of estimates is surprising. We combined incidence data from historical outbreaks of canine rabies from around the world with in-depth contact-tracing data from Tanzania to investigate initial growth rates ( r 0 ), generation-interval distributions ( G ), and reproductive numbers ( ℛ 0 ). We improved on earlier estimates by choosing outbreak windows algorithmically; fitting r 0 using a more appropriate statistical method that accounts for decreases through time; and incorporating uncertainty from both r 0 and G in our confidence intervals on ℛ 0 . Our ℛ 0 estimates are larger than previous estimates, with wider confidence intervals. These revised ℛ 0 estimates suggest that a greater level of vaccination effort will be required to eliminate rabies than previously thought, but that the level of coverage required remains feasible. Our hybrid approach for estimating ℛ 0 and its uncertainty is applicable to other disease systems where researchers estimate ℛ 0 by combining data-based estimates of r 0 and G .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".