Le caractère saisonnier du coronavirus et d’autres virus au Canada : les conséquences de la COVID-19
Bibliographic record
Abstract
Background: Like endemic coronaviruses, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is believed to have emerged in humans from a zoonotic source and may ultimately develop a seasonal pattern. A seasonal pattern, particularly if combined with other seasonal outbreaks of respiratory virus infections, may have significant impacts on the healthcare system. We evaluated the seasonal pattern of existing endemic coronaviruses and several other common respiratory viruses to determine the potential impacts of added burden of respiratory disease should SARS-CoV-2 establish seasonality. Methods: National surveillance data for laboratory confirmations of endemic coronaviruses, influenza A and B viruses, rhinovirus/enterovirus, human metapneumovirus, respiratory syncytial virus and parainfluenza virus for the past 10 years were obtained from the Government of Canada Open Data and FluWatch. Epidemic curves were generated from total case numbers and percent of samples testing positive for each respiratory virus by epidemiological week. Results: In Canada, endemic coronaviruses and other common respiratory viruses cause annual seasonal outbreaks in the winter months. Should SARS-CoV-2 develop a seasonal pattern similar to endemic coronaviruses and respiratory viruses, co-circulation would be expected to peak between January and March. Peak endemic coronavirus activity occurs during the nadir of rhinovirus/enterovirus and parainfluenza activity. Conclusion: Healthcare settings, assisted-living and long-term care homes, schools and essential services employers should anticipate and have contingencies for seasonal outbreaks of SARS-CoV-2 and co-circulating respiratory viruses during peak seasons. Given the likelihood of co-circulation, diagnostic multiplex testing targeting co-circulating pathogens may be more efficient than single target assays for symptomatic individuals if a seasonal pattern to coronavirus disease 2019 (COVID-19) is established.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".