Comparison of nucleocapsid and spike antibody ELISAs for determining SARS‐CoV‐2 seropositivity in Kenyan women and infants
Bibliographic record
Abstract
Abstract A multitude of enzyme‐linked immunosorbent assays (ELISAs) has been developed to detect severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) antibodies since the coronavirus disease 2019 pandemic started in late 2019. Assessing the reliability of these assays in diverse global populations is critical. This study compares the use of the commercially available Platelia Total Ab Assay (Bio‐Rad) nucleocapsid ELISA to the widely used Mount Sinai spike IgG ELISA in a Kenyan population seroprevalence study. Using longitudinal plasma specimens collected from a mother–infant cohort living in Nairobi, Kenya between May 2019 and December 2020, this study demonstrates that the two assays have a high qualitative agreement (92.7%) and strong correlation of antibody levels ( R 2 = 0.973) in repeated measures. Within this cohort, seroprevalence detected by either ELISA closely resembled previously published seroprevalence estimates for Kenya during the sampling period and no significant difference in the incidence of SARS‐CoV‐2 antibody detection by either assay was observed. Assay comparability was not affected by HIV exposure status. These data support the use of the Platelia SARS‐CoV‐2 Total Ab ELISA as a suitable high‐throughput method for seroprevalence studies in Kenya.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".