Use of US Blood Donors for National Serosurveillance of Severe Acute Respiratory Syndrome Coronavirus 2 Antibodies: Basis for an Expanded National Donor Serosurveillance Program
Bibliographic record
Abstract
BACKGROUND: The Recipient Epidemiology and Donor Evaluation Study-IV-Pediatric (REDS-IV-P) Epidemiology, Surveillance and Preparedness of the Novel SARS-CoV-2 Epidemic (RESPONSE) seroprevalence study conducted monthly cross-sectional testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in blood donors in 6 US metropolitan regions to estimate the extent of SARS-CoV-2 infections over time. METHODS: During March-August 2020, approximately ≥1000 serum specimens were collected monthly from each region and tested for SARS-CoV-2 antibodies using a well-validated algorithm. Regional seroprevalence estimates were weighted based on demographic differences compared with the general population. Seroprevalence was compared with reported coronavirus disease 2019 (COVID-19) case rates over time. RESULTS: For all regions, seroprevalence was <1.0% in March 2020. New York, New York, experienced the biggest increase (peak seroprevalence, 15.8% in May). All other regions experienced modest increases in seroprevalence (1%-2% in May-June to 2%-4% in July-August). Seroprevalence was higher in younger, non-Hispanic black, and Hispanic donors. Temporal increases in donor seroprevalence correlated with reported case rates in each region. In August, 1.3-5.6 estimated cumulative infections (based on seroprevalence data) per COVID-19 case were reported to the Centers for Disease Control and Prevention. CONCLUSIONS: Increases in seroprevalence were found in all regions, with the largest increase in New York. Seroprevalence was higher in non-Hispanic black and Hispanic than in non-Hispanic white blood donors. SARS-CoV-2 antibody testing of blood donor samples can be used to estimate the seroprevalence in the general population by region and demographic group. The methods derived from the RESPONSE seroprevalence study served as the basis for expanding SARS-CoV-2 seroprevalence surveillance to all 50 states and Puerto Rico.
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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 | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".