Self-reported influenza and influenza-like symptoms in U.S. adults age 18–64 between September 1, 2019 and April 15, 2020
Bibliographic record
Abstract
BACKGROUND: The Influenza-like Illness Surveillance Network (ILINet) can indicate the presence of novel, widespread community pathogens. Comparing week-to-week reported influenza-like illness percentages may identify the time of year a novel pathogen is introduced. However, changes in health-seeking behavior during the COVID-19 pandemic call in to question the reliability of 2019-2020 ILINet data as a comparison to prior years, potentially rendering this system less reliable as a novel pathogen surveillance tool. Corroboration of trends seen in the 2019-2020 ILINet data lends confidence to the validity of those trends. This study compares predicted versus reported influenza and influenza-like illnesses in vaccinated adults as a surrogate measure of novel pathogen surveillance. METHODS: An online survey was used to ask US adults their influenza vaccination status, whether they were diagnosed with influenza after vaccination, and whether they experienced an influenza-like illness other than flu. RESULTS: Prevalence of self-reported flu diagnosis in adults age 18-64 who received the flu vaccine between September 1, 2019 and April 15, 2020 (n = 3,225) was 5.8 %, while self-reported flu or flu-like illness (without a flu diagnosis) was 17.9 %. CONCLUSION: Flu and flu-like illness in this sample of flu-vaccinated U.S. adults is significantly higher than predicted, consistent with substantially higher ILI's in 2019-20 compared to ILI's from 2018-19, suggesting that the ILI values reported during the COVID-19 pandemic may be appropriate for comparison to prior years.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".