Increased household secondary attacks rates with Variant of Concern SARS-CoV-2 index cases
Bibliographic record
Abstract
Abstract IMPORTANCE Higher secondary attack rates related to variant of concern (VOC) index cases have been reported, but have not been explored within households, which continue to be an important source of coronavirus disease 2019 (COVID-19) transmission OBJECTIVE To compare secondary attack rates in households with VOC versus non-VOC index cases. DESIGN A retrospective cohort study of household index cases reported from February 7 – 27, 2021. A propensity-score matched cohort was derived to calculate adjusted estimates. SETTING Ontario, Canada. PARTICIPANTS A population-based cohort of all private households with index cases. We excluded cases in congregate settings, as well as households with one individual or with >1 case with the same earliest symptom onset date. EXPOSURE VOC status, defined as either individuals confirmed as B.1.1.7 using whole genome sequencing or those that screened positive for the N501Y mutation using real-time PCR. MAIN OUTCOME AND MEASURE Household secondary attack rate, defined as the number of household secondary cases that occurred 1-14 days after the index case divided by the total number of household secondary contacts. RESULTS We included 1,259 index VOC and non-VOC cases in the propensity score-matched analysis. The secondary attack rate for VOC index cases in this matched cohort was 1.31 times higher than non-VOC index cases (RR=1.31, 95%CI 1.14-1.49), similar to the unadjusted estimate. In stratified analyses, the higher secondary attack rate for VOC compared to non-VOC index cases was accentuated for asymptomatic index cases (RR=1.91, 95% CI 0.96-3.80) and presymptomatic cases (RR=3.41, 95%CI 1.13-10.26) CONCLUSIONS AND RELEVANCE This study provides strong evidence of increased transmissibility in households due to VOCs and suggests that asymptomatic and pre-symptomatic transmission may be of particular importance for VOCs. Our study suggests that more aggressive public health measures will be needed to control VOCs and that ongoing research is needed to understand mechanisms of VOC transmissibility to curb their associated morbidity and mortality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".