Monitoring of SARS‐CoV‐2 infection in mustelids
Bibliographic record
Abstract
American mink and ferret are highly susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but no information is available for other mustelid species. SARS-CoV-2 spreads very efficiently within mink farms once introduced, by direct and indirect contact, high within-farm animal density increases the chance for transmission. Between-farm spread is likely to occur once SARS-CoV-2 is introduced, short distance between SARS-CoV-2 positive farms is a risk factor. As of 29 January 2021, SARS-CoV-2 virus has been reported in 400 mink farms in eight countries in the European Union. In most cases, the likely introduction of SARS-CoV-2 infection into farms was infected humans. Human health can be at risk by mink-related variant viruses, which can establish circulation in the community, but so far these have not shown to be more transmissible or causing more severe impact compared with other circulating SARS-CoV-2. Concerning animal health risk posed by SARS-CoV-2 infection the animal species that may be included in monitoring plans are American mink, ferrets, cats, raccoon dogs, white-tailed deer and Rhinolophidae bats. All mink farms should be considered at risk of infection; therefore, the monitoring objective should be early detection. This includes passive monitoring (in place in the whole territory of all countries where animals susceptible to SARS-CoV-2 are bred) but also active monitoring by regular testing. First, frequent testing of farm personnel and all people in contact with the animals is recommended. Furthermore randomly selected animals (dead or sick animals should be included) should be tested using reverse transcriptase-polymerase chain reaction (RT-PCR), ideally at weekly intervals (i.e. design prevalence approximately 5% in each epidemiological unit, to be assessed case by case). Suspected animals (dead or with clinical signs and a minimum five animals) should be tested for confirmation of SARS-CoV-2 infection. Positive samples from each farm should be sequenced to monitor virus evolution and results publicly shared.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".