Lessons Learned from the COVID-19 Pandemic and How Blood Operators Can Prepare for the Next Pandemic
Bibliographic record
Abstract
Humans interact with virus-infected animal hosts, travel globally, and maintain social networks that allow for novel viruses to emerge and develop pandemic potential. There are key lessons-learned from the coronavirus diseases 2019 (COVID-19) pandemic that blood operators can apply to the next pandemic. Warning signals to the COVID-19 pandemic included outbreaks of Severe acute respiratory syndrome-related coronavirus-1 (SARS-CoV-1) and Middle East respiratory syndrome-related coronavirus (MERS-CoV) in the prior two decades. It will be critical to quickly determine whether there is a risk of blood-borne transmission of a new pandemic virus. Prior to the next pandemic blood operators should be prepared for changes in activities, policies, and procedures at all levels of the organization. Blood operators can utilize "Plan-Do-Study-Act" cycles spanning from: vigilance for emerging viruses, surveillance activities and studies, operational continuity, donor engagement and trust, and laboratory testing if required. Occupational health and donor safety issues will be key areas of focus even if the next pandemic virus is not transfusion transmitted. Blood operators may also be requested to engage in new activities such as the development of therapeutics or supporting public health surveillance activities. Activities such as scenario development, tabletop exercises, and drills will allow blood operators to prepare for the unknowns of the next pandemic.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".