Serological investigation of asymptomatic cases of SARS-CoV-2 infection reveals weak and declining antibody responses
Bibliographic record
Abstract
Without an effective vaccine against SARS-CoV-2, the build-up of herd immunity through natural infection has been suggested as a means to control COVID-19. Although population immunity is typically estimated by the serological investigation of recovered patients, humoral immunity in asymptomatic subjects has not been well studied, although they represent a large proportion of all SARS-CoV-2 infection cases. In this study, we conducted a serosurvey of asymptomatic infections among food workers and performed serological and cellular response analyses of asymptomatic subjects in Wuhan, the original epicenter of the COVID-19 outbreak. Our data showed that up to 5.91% of the food workers carried SARS-CoV-2 IgG antibodies asymptomatically; however, in 90.4% of them, the antibody level declined over a 2-week period. IgM and IgG antibodies, including neutralizing antibodies, were significantly lower in asymptomatic subjects than in recovered symptomatic patients with similar disease courses. Furthermore, the asymptomatic subjects showed lymphopenia and a prominent decrease in the B-cell population, as well as a low frequency of antibody-secreting cells and a low cytokine response. These factors probably contributed to the low and unsustained antibody levels in asymptomatic subjects. Our results show that asymptomatic subjects are likely to be vulnerable to SARS-CoV-2 reinfection, and neither the proportion of population immunity nor the breadth of immune responses is sufficient for herd immunity.
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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 | 0.000 | 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) | 0.002 | 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".