Antibody reactivity against SARS-CoV-2 in adults from the Vancouver metropolitan area, Canada
Bibliographic record
Abstract
BACKGROUND: Pre-existing antibody reactivity against SARS-CoV-2 in unexposed people is a potentially important consideration for COVID-19 severity and vaccine responses. However, it has been difficult to quantify due to a lack of reliable defined background titers in unexposed individuals. METHODS: We measured IgG against multiple SARS-CoV-2 antigens, SARS-CoV and other circulating coronavirus spike proteins using a highly sensitive multiplex assay, and total SARS-CoV-2 spike-specific antibodies (IgG/M/A) using a commercial CLIA assay in 276 adults from the Vancouver area, Canada between May 17th and June 19th 2020. Reactivity threshold in unexposed individuals were defined comparing to pre-pandemic sera and to sera from infants under 6 months of age. RESULTS: The seroprevalence from a SARS-CoV-2 exposure, adjusted for false-positive and false-negative test results, was 0.60% in our adult cohort. High antibody reactivity to circulating endemic coronaviruses was observed in all adults and was about 10-fold lower in infants under 6 months. Consistent with a waning of maternal antibodies, reactivity in infants decreased more than 50-fold eight months later. SARS-CoV-2 Spike, RBD, NTD or nucleocapsid antibody reactivity >100-fold above that of older infants was detected in the vast majority of unexposed adults and pre-pandemic sera. This antibody reactivity correlated with titers against circulating coronaviruses, but not with age, sex, or whether adults were healthcare workers. CONCLUSION: A majority of unexposed adults have pre-existing antibody reactivity against SARS-CoV-2. The lack of similar antibody reactivity in infants where maternal antibodies have waned suggests that this cross-reactivity is acquired, likely from repeated exposures to circulating coronaviruses.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".