Retrospective case study of the impacts of multiple One Health oriented biocontainment research facilities during the SARS-CoV-2 pandemic
Bibliographic record
Abstract
The SARS-CoV-2 pandemic revealed the importance of rapidly identifying and controlling zoonotic diseases and underscored the necessity of coordinating and planning pandemic preparedness with comprehensive one health strategies to prevent and control the emergence and transmission of zoonotic pathogens. The present case study catalogued the scope and range of activities performed by the biocontainment research facilities that ultimately comprised the Research Alliance for Veterinary Science and BSL-3 Biodefense Network (RAV3N) created during SARS-CoV-2 pandemic. Results revealed that nearly all RAV3N members directly contributed to all aspects of the response against the pandemic, from human diagnostic testing to specialized animal disease models for developing medical countermeasures to investigating the potential for pets and wildlife to serve as potential reservoirs for the SARS-CoV-2. Reflecting their expertise, approximately 80 % of members developed multiple animal models as part of their SARS-CoV-2 research. RAV3N members investigated basic virology, transmission, and host susceptibility in animal models ranging from non-human primates and livestock, to wildlife, arthropods, and mice. Approximately half of member institutions provided SARS-CoV-2 diagnostic testing services and/or environmental wastewater testing and surveillance to augment limited public health laboratory capacity during the pandemic. State and Federal sources funded and authorized all the reported response activities, however only 40 % of these response activities were coordinated with local public health officials. A major recommendation is to improve direct communication and pandemic response planning between the veterinary science and zoonotic disease and human public health communities. RAV3N provides a model for sharing information and coordinating response activities between veterinary science and public health officials in future disease outbreaks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".