Persistence and decay of neutralizing antibody responses elicited by SARS-CoV-2 infection and hybrid immunity in a Canadian cohort
Bibliographic record
Abstract
A major challenge with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has been assessing the intensity, dynamics, and determinants of the antibody responses after infection and/or vaccination. Therefore, we aimed to characterize the longitudinal dynamics of the antibody responses among naturally infected individuals and individuals who achieved hybrid immunity in a large Canadian cohort. We demonstrate that anti-Spike IgGs and neutralizing antibody dynamics vary greatly among individuals with COVID-19, in peak antibody levels, rate of waning, and longevity of the antibody response. Additionally, we found an association between robust antibody responses and individuals with severe COVID-19 clinical symptoms during the first-month post-symptom onset. For individuals who achieved hybrid immunity, a robust increase in anti-S1 IgGs and neutralizing antibodies followed the first vaccination dose; however, there was a minimal increase in the anti-S1 IgGs and neutralizing antibody titers after administration of the second dose of the vaccine. Furthermore, neutralizing antibodies elicited by the wild-type virus alone were largely ineffective against emerging variants of concern in our natural infection-only cohort, in contrast to a much broader and more robust neutralization profile observed in individuals who achieved hybrid immunity. Our findings emphasize the need for global SARS-CoV-2 vaccination efforts to further sustain protective immune responses required to minimize viral spread and disease severity in the population. As SARS-CoV-2 variants continue to emerge, understanding the interplay between previous infections, vaccine durability, and virus evolution will be critical for guiding ongoing vaccination strategies. IMPORTANCE: A major challenge with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has been assessing the intensity, dynamics, and determinants of the antibody response after infection and/or vaccination. Our paper addresses this in a large Canadian cohort with antibody responses that were generated by natural infection as well as vaccine in some persons studied.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".