Rabies surveillance in the United States during 2020
Bibliographic record
Abstract
OBJECTIVE: To provide epidemiological information on animal and human cases of rabies in the US during 2020 and summaries of 2020 rabies surveillance for Canada and Mexico. ANIMALS: All animals submitted for laboratory diagnosis of rabies in the US during 2020. PROCEDURES: State and territorial public health departments and USDA Wildlife Services provided 2020 rabies surveillance data. Data were analyzed temporally and geographically to assess trends in domestic and wildlife rabies cases. RESULTS: During 2020, 54 jurisdictions submitted 87,895 animal samples for rabies testing, of which 85,483 (97.3%) had a conclusive (positive or negative) test result. Of these, 4,479 (5.2%) tested positive for rabies, representing a 4.5% decrease from the 4,690 cases reported in 2019. Texas (n = 580 [12.9%]), Pennsylvania (371 [8.3%]), Virginia (351 [7.8%]), New York (346 [7.7%]), North Carolina (301 [6.7%]), New Jersey (257 [5.7%]), Maryland (256 [5.7%]), and California (248 [5.5%]) together accounted for > 60% of all animal rabies cases reported in 2020. Of the total reported rabid animals, 4,090 (91.3%) involved wildlife, with raccoons (n = 1,403 [31.3%]), bats (1,400 [31.3%]), skunks (846 [18.9%]), and foxes (338 [7.5%]) representing the primary hosts confirmed with rabies. Rabid cats (288 [6.4%]), cattle (43 [1.0%]), and dogs (37 [0.8%]) accounted for 95% of rabies cases involving domestic animals in 2020. No human rabies cases were reported in 2020. CONCLUSIONS AND CLINICAL RELEVANCE: For the first time since 2006, the number of samples submitted for rabies testing in the US was < 90,000; this is thought to be due to factors related to the COVID-19 pandemic, as similar decreases in sample submission were also reported by Canada and Mexico.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".