Assessment of an enhanced COVID-19 case and contact management protocol in controlling a SARS-CoV-2 Alpha (B.1.1.7) variant outbreak on a construction site
Bibliographic record
Abstract
BACKGROUND: To control the spread of SARS-CoV-2 variants of concern (VOCs), Kingston, Frontenac, and Lennox & Addington (KFL&A) Public Health implemented a more stringent COVID-19 case and contact management (CCM) protocol than what was used across Ontario at the time. We describe epidemiological data and public health measures employed during one of the largest COVID-19 outbreaks in the KFL&A region at the time, caused by the SARS-CoV-2 Alpha (B.1.1.7) VOC, to assess this enhanced protocol. METHODS: We obtained line lists of workers associated with the construction site outbreak, and subsequent cases and contacts from case investigators. Case testing, mutation status, and whole genome sequencing were conducted by Public Health Ontario Laboratories. RESULTS: From 409 high-risk contacts of the outbreak, 109 (27%) developed COVID-19. Three generations of spread were associated with the outbreak, affecting seven public health regions across three provinces. Using an enhanced approach to the CCM, KFL&A Public Health caught 15 cases that could have been missed by standard provincial protocols. CONCLUSIONS: Rapid initial spread within the construction site produced a relatively high attack rate among workers (26%) and their immediate contacts (34%). KFL&A Public Health's implementation of stringent CCM protocols and fast testing turn-around time effectively curbed the spread of the disease in subsequent generations - illustrated by the large reduction in attack rate (34%-14%) and cases (50-10) between the second and third generations. Lessons learned from this analysis may inform guidance on the CCM for future SARS-CoV-2 VOCs as well as other highly transmissible communicable diseases.
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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 | 0.077 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".