Comparative Household Secondary Attack Rates associated with B.1.1.7, B.1.351, and P.1 SARS-CoV-2 Variants
Bibliographic record
Abstract
Abstract Background The emergence of SARS-CoV-2 variants associated with increased transmissibility are driving a 3 rd global surge in COVID-19 incidence. There are currently few reliable estimates for the P.1 and B.1.351 lineages. We sought to compare the secondary attack rates of SARS-COV-2 mutations and variants in Canada’s largest province of Ontario, using a previously validated household-based approach. Methods We identified individuals with confirmed SARS-CoV-2 infection in Ontario’s provincial reportable disease surveillance system. Cases were grouped into households based on reported residential address. Index cases had the earliest of symptom onset in the household. Household secondary attack rate was defined as the percentage of household contacts identified as secondary cases within 1-14 days after the index case. Results We identified 26,888 index household cases during the study period. Among these, 7,555 (28%) were wild-type, 17,058 (63%) were B.1.1.7, 1674 (6%) were B.1.351 or P.1, and 601 (2%) were non-VOC mutants (Table 1). The secondary attack rates, according to index case variant were as follows: 20.2% (wild-type), 25.1% (B.1.1.7), 27.2% (B.1.351 or P.1), and 23.3% (non-VOC mutants). In adjusted analyses, we found that B.1.1.7, B.1.351, and P.1 index cases had the highest transmissibility (presumptive B.1.1.7 OR adjusted =1.49, 95%CI 1.36, 1.64; presumptive B.1.351 or P.1 OR adjusted =1.60, 95%CI 1.37, 1.87). Discussion Substantially higher transmissibility associated with variants will make control of SARS-CoV-2 more difficult, reinforcing the urgent need to increase vaccination rates globally.
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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 | 0.001 | 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) | 0.002 | 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".