An international comparison of anti‐SARS‐COV‐2 assays used for seroprevalence surveys from blood component providers
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Access to large pools of healthy adult donors advantageously positions blood component providers to undertake anti-SARS-CoV-2 seroprevalence studies. While numerous seroprevalence reports have been published by blood operators during the COVID-19 pandemic, details on the assay used has not been well documented. The objectives of this study were to evaluate the diversity of assays being used by blood operators and assess how this may affect seroprevalence estimates. MATERIALS AND METHODS: We surveyed 49 blood component providers from 39 countries. Questionnaire included information on the number and identity of assays used, the detected immunoglobulin(s) and target antigen, and performance characteristics (sensitivity, specificity). RESULTS: Thirty-eight of the 49 contacted blood suppliers provided at least partial responses. The results indicate that 19 commercial and five in-house serology assays have been used by surveyed blood operators. The Abbott SARS-CoV-2 IgG assay was the most commonly used kit and utilized by 15 blood suppliers. Two assays did not detect IgG, but detected either IgM/IgA or IgM. 68·2% of assays targeted the spike protein and 50% the nucleocapsid protein, while 18·2% targeted both viral proteins. The sensitivity and specificity of IgG-specific assays ranged from 71·9% to 100% and from 96·2% to 100%, respectively. As of 18 October 2020, the seroprevalence was below 5% in 10 of 14 countries reporting. CONCLUSION: Our results highlight the diversity of assays being used. Analyses comparing blood donor seroprevalence across countries should consider assay characteristics with optimization of signal/cut-off ratios and consistent methodology to adjust for waning antibody.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".