The disappearing COVID-Naïve Population and comparative Roche vs. Abbott Test sensitivity: evidence from antibody seroprevalence in Milwaukee County, Wisconsin
Bibliographic record
Abstract
We study the prevalence of SARS-CoV-2 antibodies in a diverse population in Milwaukee County, Wisconsin from May 2021 to June 2022. We find that 99.4% (523/526) of the participants had positive results for antibodies to the SARS CoV2 spike protein over April-June 2022, soon after the early-2022 Omicron surge. Positive tests for spike protein antibodies were very high (86%; 19/22) even among unvaccinated persons who reported no knowledge of prior infection. Thus, by mid-2022, almost all persons were no longer COVID-naïve, defined as vaccination, infection (often without symptoms), or both. Nucleocapsid antibody tests, especially the Abbott test, were far less sensitive than spike protein tests, and Abbott test sensitivity faded with time since infection. Thus, studies which rely on nucleocapsid tests will understate prior infection rates. We also report large sample evidence on the performance of the Abbott and Roche spike and nucleocapsid protein tests in capturing prior vaccination, infection, or both. The Roche spike protein test outperforms the Abbott spike test, and the Roche nucleocapsid test greatly outperforms the Abbott nucleocapsid test.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".