Virology and immune dynamics reveal high household transmission of ancestral SARS-CoV-2 strain
Bibliographic record
Abstract
Background Household studies are crucial for understanding the transmission of SARS-CoV-2 infection, which may be underestimated from PCR testing of respiratory samples alone. We aim to combine assessment of household mitigation measures; nasopharyngeal, saliva and stool PCR testing; along with mucosal and systemic SARS-CoV-2 specific antibodies, to comprehensively characterise SARS-CoV-2 infection and transmission in households. Methods Between March and September 2020, we obtained samples from 92 participants in 26 households in Melbourne, Australia, in a 4-week period following onset of infection with ancestral SARS-CoV-2 variants. Results The secondary attack rate was 36% (24/66) when using nasopharyngeal swab (NPS) PCR positivity alone. However, when respiratory and non-respiratory samples were combined with antibody responses in blood and saliva, the secondary attack rate was 76% (50/66). SARS-CoV-2 viral load of the index case and household isolation measures were key factors that determine secondary transmission. In 27% (7/26) of households, all family members tested positive by NPS for SARS-CoV-2 and were characterised by lower respiratory Ct-values than low transmission families (Median 22.62 vs 32.91; IQR 17.06 to 28.67 vs 30.37 to 34.24). High transmission families were associated with enhanced plasma antibody responses to multiple SARS-CoV-2 antigens and the presence of neutralising antibodies. Three distinguishing saliva SARS-CoV-2 antibody features were identified according to age (IgA1 to Spike 1, IgA1 to nucleocapsid protein (NP), suggesting that adults and children generate distinct mucosal antibody responses during the acute phase of infection. Conclusion Utilising respiratory and non-respiratory PCR testing, along with measurement of SARS-CoV-2 specific local and systemic antibodies, provides a more accurate assessment of infection within households and highlights some of the immunological differences in response between children and adults.
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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 | 0.001 |
| 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".