COVID-19 farm outbreaks in Ontario, January - December 2020
Bibliographic record
Abstract
Background: Farm workers are critical to Ontario's food supply chain as they grow and harvest the food that Ontario relies on; however, they are subject to several occupation-related coronavirus disease 2019 (COVID-19) transmission risk factors. We describe the epidemiology of farm outbreaks in Ontario over the first calendar year of the pandemic and explore trends in outbreaks by season and type of farm. Methods: Data pertaining to farm outbreaks in Ontario from January 1 to December 31, 2020, and their associated laboratory-confirmed cases were extracted from the provincial database. Outbreaks were characterized by size, season, farm type and duration. Cases were characterized by age, gender, medical risk factors, clinical presentation and outcomes. Results: There were 64 farm outbreaks associated with 2,202 confirmed cases of COVID-19 in Ontario during 2020. The majority of outbreaks occurred in spring (n=25, 39.1%) and fall (n=25, 39.1%). The fewest outbreaks occurred in the summer (n=6, 9.4%), corresponding with low community rates during that time, and the majority of these were in greenhouse farms (n=5, 83.3%). The median outbreak size was 14.5 cases (range: 1-240), and the median duration was 23 days (range: 0-128). Among cases, most were male (83.2%), the median age was 35 years, 10.0% had one or more comorbidities, 31.2% were asymptomatic, 16 required hospitalization and three died. Conclusion: Farm outbreaks were a source of COVID-19 transmission and illness in 2020, particularly in the spring and fall. Outbreaks continued in greenhouse farms despite lower summer community transmission.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".