Rabies surveillance in the United States during 2022
Bibliographic record
Abstract
OBJECTIVE: To provide comprehensive epidemiological information about the distribution and occurrence of rabies during 2022 in the US, Canada, and Mexico. METHODS: The US National Rabies Surveillance System collected 2022 animal rabies data from US state and territorial public health departments and USDA Wildlife Services. Temporal and geographic analyses were conducted to evaluate trends in animal rabies cases. RESULTS: During 2022, 54 US jurisdictions reported 3,579 animal rabies cases, reflecting a 2.3% decline from 3,663 cases reported in 2021. Six states collectively reported > 50% of animal rabies cases: Texas (395 [11.0%]), Virginia (337 [9.4%]), Pennsylvania (329 [9.2%]), New York (267 [7.5%]), North Carolina (264 [7.4%]), and California (241 [6.7%]). Out of the total reported rabies animal cases, 3,234 (90.4%) were attributed to wildlife, with bats (1,218 [34.0%]), raccoons (1,014 [28.3%]), skunks (660 [18.4%]), and foxes (269 [7.5%]) representing the primary hosts confirmed with rabies. Rabid cats (222 [6.2%]), cattle (42 [1.2%]), and dogs (50 [1.4%]) constituted > 90% of reported domestic animal rabies cases. CONCLUSIONS: In 2022, there was an increase in the number of animal samples submitted for rabies testing in the US and Canada. A notable geographic expansion of gray fox rabies virus variant was detected in the US. Three human rabies deaths due to vampire bat rabies infection occurred in Mexico; none were reported from the US and Canada. CLINICAL RELEVANCE: Laboratory diagnosis of rabies in animals is critical to ensure judicious use of human rabies postexposure prophylaxis.
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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 | 0.004 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".