Public Veterinary Medicine: Public Health: Rabies surveillance in the United States during 2018
Bibliographic record
Abstract
OBJECTIVE: To describe rabies and rabies-related events occurring during 2018 in the United States. ANIMALS: All animals submitted for laboratory diagnosis of rabies in the United States during 2018. PROCEDURES: State and territorial public health departments provided data on animals submitted for rabies testing in 2018. Data were analyzed temporally and geographically to assess trends in domestic animal and wildlife rabies cases. RESULTS: During 2018, 54 jurisdictions reported 4,951 rabid animals to the CDC, representing an 11.2% increase from the 4,454 rabid animals reported in 2017. Texas (n = 695 [14.0%]), Virginia (382 [7.7%]), Pennsylvania (356 [7.2%]), North Carolina (332 [6.7%]), Colorado (328 [6.6%]), and New York (320 [6.5%]) together accounted for almost half of all rabid animals reported in 2018. Of the total reported rabies cases, 4,589 (92.7%) involved wildlife, with bats (n = 1,635 [33.0%]), raccoons (1,499 [30.3%]), skunks (1,004 [20.3%]), and foxes (357 [7.2%]) being the major species. Rabid cats (n = 241 [4.9%]) and dogs (63 [1.3%]) accounted for > 80% of rabid domestic animals reported in 2018. There was a 4.6% increase in the number of samples submitted for testing in 2018, compared with the number submitted in 2017. Three human rabies deaths were reported in 2018, compared with 2 in 2017. CONCLUSIONS AND CLINICAL RELEVANCE: The overall number of animal rabies cases increased from 2017 to 2018. Laboratory diagnosis of rabies in animals is critical to ensure that human rabies postexposure prophylaxis is administered judiciously.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".