Use of U.S. Blood Donors for National Serosurveillance of SARS-CoV-2 Antibodies: Basis for an Expanded National Donor Serosurveillance Program
Bibliographic record
Abstract
Abstract Introduction The REDS-IV-P Epidemiology, Surveillance and Preparedness of the Novel SARS-CoV-2 Epidemic (RESPONSE) seroprevalence study conducted monthly cross-sectional testing for SARS-CoV-2 antibodies on blood donors in six U.S. metropolitan regions to estimate the extent of SARS-COV-2 infections over time. Study Design/Methods During March-August 2020, approximately ≥1,000 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 with the general population. Seroprevalence was compared with reported COVID-19 case rates over time. Results/Findings For all regions, seroprevalence was <1.0% in March 2020. 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 reported to CDC. Conclusion Increases in seroprevalence were found in all regions, with the largest increase in New York. Seroprevalence was higher in non-Hispanic Black and Hispanic blood donors 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. Summary SARS-CoV-2 serosurveillance data from blood donors in 6 US regions were used to estimate population weighted seroprevalence. Seroprevelance rates were higher in case rates. The study was expanded to a national donor serosurveillance program. Disclaimer The content is solely the responsibility of the authors and does not represent the policy of the National Institutes of Health or the Department of Health and Human Services. Any specific brandnames included in this manuscript are for identification purposes only and are not intended to represent an endorsement by CDC. The findings and conclusions in this report are those of the authorsand do not necessarily represent the official position of the Centers of Disease Control and Prevention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".