SARS‐CoV‐2 and wastewater: What does it mean for non‐human primates?
Bibliographic record
Abstract
In most of our lifetimes, we have not faced a global pandemic such as the novel coronavirus disease 2019. The world has changed as a result. However, it is not only humans who are affected by a pandemic of this scale. Our closest relatives, the non-human primates (NHPs) who encounter researchers, sanctuary/zoo employees, and tourists, are also potentially at risk of contracting the virus from humans due to similar genetic susceptibility. "Anthropozoonosis"-the transmission of diseases from humans to other species-has occurred historically, resulting in infection of NHPs with human pathogens that have led to disastrous outbreaks. Recent studies have assessed the susceptibility of NHPs and predict that catarrhine primates and some lemurs are potentially highly susceptible to infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. There is accumulating evidence that a new factor to consider with the spread of the virus is fecal-oral transmission. The virus has been detected in the watersheds of countries with underdeveloped infrastructure where raw sewage enters the environment directly without processing. This may expose NHPs, and other animals, to SARS-CoV-2 through wastewater contact. Here, we address these concerns and discuss recent evidence. Overall, we suggest that the risk of transmission of SARS-CoV-2 via wastewater is low. Nonetheless, tracking of viral RNA in wastewater does provide a unique testing approach to help protect NHPs at zoos and wildlife sanctuaries. A One Health approach going forward is perhaps the best way to protect these animals from a novel virus, the same way that we would protect ourselves.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".