COVID-19 Is Not the Flu: Four Graphs From Four Countries
Bibliographic record
Abstract
Background: COVID-19 has caused a global public health emergency. Government mitigation strategies included a series of behavior-based prevention policies that had a likely impact on the spread of other contagious respiratory illnesses, such as seasonal influenza. Our aim was to explore how 2019–2020 influenza tracked onto COVID-19 pandemic and its mitigation methods. Materials and Methods: We linked the WHO FluNet database and COVID-19 confirmed cases (Johns Hopkins University) for four countries across the northern (Canada, the United States) and southern hemispheres (Australia, Brazil) for the period 2016–2020. Graphical presentations of longitudinal data were provided. Results: There was a notable reduction in influenza cases for the 2019–2020 season. Northern hemisphere countries experienced a quicker ending to the 2019–2020 seasonal influenza cases (shortened by 4–7 weeks) and virtually no 2020 fall influenza season. Countries from the southern hemisphere experienced drastically low levels of seasonal influenza, with consistent trends that were approaching zero cases after the introduction of COVID-19 measures. Conclusions: It is likely that the COVID-19 mitigation measures played a notable role in the marked decrease in influenza, with little to no influenza activity in both the northern and southern hemispheres. In spite of this reduction in influenza cases, there was still community spread of COVID-19, highlighting the contagiousness of SARS-CoV-2 compared to influenza. These results, together with the higher mortality rate from SARS-CoV-2 compared to influenza, highlight that COVID-19 is a far greater health threat than influenza.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".