Rabies surveillance in the United States during 2023
Bibliographic record
Abstract
Objective: Describe the epidemiologic landscape of rabies and rabies testing in the US during 2023 and provide an overview of rabies in Canada and Mexico. Methods: The US National Rabies Surveillance System collects monthly animal rabies testing data from 54 reporting jurisdictions. Data reported in 2023 were analyzed geographically and temporally to explore trends in animal rabies cases by rabies virus variant. Results: In 2023, 3,760 cases of animal rabies were reported to the National Rabies Surveillance System (4.2% of 89,530 samples submitted), representing a 5.1% increase from 2022. Of positive samples, 309 (8.2%) were domestic animals, and 3,451 (91.8%) were wildlife. Cats (222 [1.2%]) and bats (1,298 [5.1%]) followed by raccoons (1,085 [10.4%]) were the most frequently found rabid domestic and wild animals. No human rabies cases were reported in the US, Canada, or Mexico in 2023. Conclusions: While a year with zero human rabies cases reported in North America represents a major success of public and animal health programs, the danger of exposure to and dying from rabies is still present. Translocation events pose a unique risk, highlighted by events during 2023. Due to the response of public and animal health agencies, the health of the public and local animals was protected. Continued effort to maintain One Health reporting mechanisms is key to the security of public and animal health. Clinical Relevance: The National Rabies Surveillance System plays a crucial role in protecting public and animal health by monitoring rabies trends in the US.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".