Characteristics Associated With Household Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Ontario, Canada: A Cohort Study
Bibliographic record
Abstract
BACKGROUND: Within-household transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been identified as one of the main sources of spread of coronavirus disease 2019 (COVID-19) after lockdown restrictions and self-isolation guidelines are implemented. Secondary attack rates among household contacts are estimated to be 5-10 times higher than among non-household contacts, but it is unclear which individuals are more prone to transmit infection within their households. METHODS: Using address matching, a cohort was assembled of all individuals with laboratory-confirmed COVID-19 residing in private households in Ontario, Canada. Descriptive analyses were performed to compare characteristics of cases in households that experienced secondary transmission versus those that did not. Logistic regression models were fit to determine index case characteristics and neighborhood characteristics associated with transmission. RESULTS: Between January and July 2020, there were 26 714 individuals with COVID-19 residing in 21 226 households. Longer testing delays (≥5 vs 0 days; odds ratio [OR], 3.02; 95% confidence interval [CI], 2.53-3.60) and male gender (OR, 1.28; 95% CI, 1.18-1.38) were associated with greater odds of household secondary transmission, while being a healthcare worker (OR, .56; 95% CI, .50-.62) was associated with lower odds of transmission. Neighborhoods with larger average family size and a higher proportion of households with multiple persons per room were also associated with greater odds of transmission. CONCLUSIONS: It is important for individuals to get tested for SARS-CoV-2 infection as soon as symptoms appear, and to isolate away from household contacts; this is particularly important in neighborhoods with large family sizes and/or crowded households.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".