Active surveillance of cats and dogs from households with human COVID-19 cases reveals over one quarter of pets infected with SARS-CoV-2 in 2020-2021 in Texas, United States
Bibliographic record
Abstract
Abstract Households where people have COVID-19 are high risk environments for companion animals that are susceptible to SARS-CoV-2. We sampled 579 pets from 281 households with one or more laboratory-confirmed person with COVID-19 in central Texas from June 2020 to May 2021. Nineteen out of 396 (4.8%) dogs and 21 out of 157 (13.4%) cats were positive for SARS-CoV-2 by RT-qPCR. Additionally, 95/382 (25%) dogs and 52/146 (36%) cats harbored SARS-CoV-2 neutralizing antibodies. Twenty-six companion animals of ten other species were negative. Overall, 164 (29%) pets were positive for SARS-CoV-2 by molecular and/or serological tests; a total of 110 (39%) out of 281 households had at least one animal with active or past SARS-CoV-2 infection. Cats were more likely to be infected by SARS-CoV-2 and had higher endpoint antibody titers than dogs. Through viral isolation from a subset of respiratory swabs, we documented 6 different lineages in dogs and cats, including the B.1.1 lineage in a cat one month prior to the first known human case in the country. We observed animal and human-pet interaction factors associated with higher risk of infection for dogs and cats, such as days after COVID-19 diagnosis and sharing food. Frequency of clinical signs of disease reported by owners of pets with active infections did not differ from uninfected ones, suggesting that not all reported signs are attributed to SARS-CoV-2 infection. Characterizing animal infections using active SARS-CoV-2 surveillance in pets at risk of infection may aid in One Health pandemic prevention, response, and management.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".