Serological Markers of SARS-CoV-2 Reinfection
Bibliographic record
Abstract
As public health and social distancing guidelines loosen in the setting of waning global natural and vaccine immunity, a deeper understanding of the immunological response to reexposure and reinfection to this highly contagious pathogen is necessary to maintain public health. Viral sequencing analysis provides a robust but unrealistic means to monitor reinfection globally. The identification of scalable pathogen-specific biomarkers of reexposure and reinfection, however, could significantly accelerate our capacity to monitor the spread of the virus through naive and experienced hosts, providing key insights into mechanisms of disease attenuation. Using a nonhuman primate model of controlled SARS-CoV-2 reexposure, we deeply probed the humoral immune response following rechallenge with various doses of viral inocula. We identified virus-specific humoral biomarkers of reinfection, with significant increases in antibody titer and function upon rechallenge across a range of humoral features, including IgG1 to the receptor binding domain of the spike protein of SARS-CoV-2 (RBD), IgG3 to the nucleocapsid protein (N), and FcγR2A receptor binding to anti-RBD antibodies. These features not only differentiated primary infection from reexposure and reinfection in monkeys but also were recapitulated in a sequencing-confirmed reinfection patient and in a cohort of putatively reinfected humans that evolved a PCR-positive test in spite of preexisting seropositivity. As such, this cross-species analysis using a controlled primate model and human cohorts reveals increases in antibody titers as promising cross-validated serological markers of reinfection and reexposure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".