Rabies surveillance in the United States during 2019
Bibliographic record
Abstract
OBJECTIVE: To provide epidemiological information on animal and human cases of rabies occurring in the United States during 2019 and summaries of 2019 rabies surveillance for Canada and Mexico. ANIMALS: All animals submitted for laboratory diagnosis of rabies in the United States during 2019. PROCEDURES: State and territorial public health departments and USDA Wildlife Services provided data on animals submitted for rabies testing in the United States during 2019. Data were analyzed temporally and geographically to assess trends in domestic and wildlife rabies cases. RESULTS: During 2019, 53 jurisdictions submitted 97,523 animal samples for rabies testing, of which 94,770 (97.2%) had a conclusive (positive or negative) test result. Of these, 4,690 tested positive for rabies, representing a 5.3% decrease from the 4,951 cases reported in 2018. Texas (n = 565 [12.0%]), New York (391 [8.3%]), Virginia (385 [8.2%]), North Carolina (315 [6.7%]), California (276 [5.9%]), and Maryland (269 [5.7%]) together accounted for almost half of all animal rabies cases reported in 2019. Of the total reported rabid animals, 4,305 (91.8%) were wildlife, with raccoons (n = 1,545 [32.9%]), bats (1,387 [29.6%]), skunks (915 [19.5%]), and foxes (361 [7.7%]) as the primary species confirmed with rabies. Rabid cats (n = 245 [5.2%]) and dogs (66 [1.4%]) accounted for > 80% of rabies cases involving domestic animals in 2019. No human rabies cases were reported in 2019. CONCLUSIONS AND CLINICAL RELEVANCE: The overall number of animal rabies cases decreased from 2018 to 2019. 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 | 0.003 | 0.004 |
| 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".