Changing contact patterns in Newfoundland and Labrador, Canada in response to public health measures during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract The provincial government of Newfoundland and Labrador, Canada implemented a contact tracing program as part of a containment strategy during the COVID-19 pandemic. A high proportion of cases were detected and contact traced, and our analysis provides insights into secondary case distributions and contact patterns in Newfoundland and Labrador. We used a heuristic approximation of secondary cases to account for ambiguities in who infected whom. These approximate values provide an empirical distribution of secondary cases. These distributions are compared against the stringency of public health measures. Additionally, we visualised age- and contact-based patterns and compared these patterns with respect to stringency. The maximum number of contacts traced per week was 4,645 and the mean number of contacts traced per case was 12.5. Approximate 95% CIs of the effective reproduction number under Alert levels 2-4 were (1.02,1.21), (0.99,1.39), (0.84,1.06), and (1.20,1.47). We find that this level of contact tracing was sufficient, in combination with other public health interventions, to contain pandemic SARS-CoV-2 spread in Newfoundland and Labrador prior to the establishment of the Omicron variant. Understanding age-based contact patterns is necessary to describe disease spread and the risk of severe outcomes. A successful containment strategy requires that contact tracing capacity is not exceeded, making it necessary to understand the behaviour of high-contact individuals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".