Interplay of Influenza A/B Subtypes and COVID-19
Bibliographic record
Abstract
Influenza, or most commonly termed the flu, is a common respiratory illness caused by viral infection. The circulation of this virus is found year-round but is more common during the flu season: fall and winter. In the United States, the number of reported cases begins to rise in October, reaches a peak in December, and returns to normal in April. Even though there are four subtypes of the Influenza virus, the seasonal flu outbreaks in humans are caused by type A and B viruses. eVision utilizes influenza data provided by the United States Center for Disease Control and Prevention (CDC) and the World Health Organization (WHO) to analyze influenza A and B cases throughout the flu season. During the 2019–20 flu season, the positive influenza cases reported in the US were between 36 and 56 million, which is the highest over the past six years. However, during the 2020–21 flu season which is the first complete flu season within the COVID-19 pandemic, the reported flu cases reduced drastically to 1,899; of which 713 were caused by influenza A viruses, and 1,186 by influenza B viruses. This indicates that the number of flu B cases was higher than that of flu A which was not normally the case prior to the COVID-19 pandemic. It was further observed that flu B reached its peak either at the same time or earlier than flu A which is also unusual compared to the flu trends prior to the onset of the COVID-19 pandemic. This peculiar trend is also noted during the Severe Acute Respiratory Syndrome (SARS) outbreak in 2003. This paper reports the findings on deviation in the Influenza type A and type B trends during the circulation of Coronavirus in the US and Canada and provides possible reasons for these changes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".