Development and comparative evaluation of SARS-CoV-2 S-RBD and N based ELISA tests in various African endemic settings
Bibliographic record
Abstract
Management of the COVID-19 pandemic relies on molecular diagnostic methods supported by serological tools. Herein, we developed S-RBD- and N- based ELISA assays useful for infection rate surveillance as well as the follow-up of acquired protective immunity against SARS-CoV-2. ELISA assays were optimized using COVID-19 Tunisian patients' sera and prepandemic controls. Assays were further validated in 3 African countries with variable endemic settings. The receiver operating curve was used to evaluate the assay performances. The N- and S-RBD-based ELISA assays performances, in Tunisia, were very high (AUC: 0.966 and 0.98, respectively, p < 0.0001). Cross-validation analysis showed similar performances in different settings. Cross-reactivity, with malaria infection, against viral antigens, was noticed. In head-to-head comparisons with different commercial assays, the developed assays showed high agreement. This study demonstrates, the added value of the developed serological assays in low-income countries, particularly in ethnically diverse populations with variable exposure to local endemic infectious diseases.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".