Could seasonal influenza vaccination influence COVID-19 risk?
Bibliographic record
Abstract
Abstract Background With possible resurgence of the SARS-CoV-2 and low seasonal influenza virus circulation next winter, reviewing evidence on a possible interaction between influenza vaccination and COVID-19 risk is important. Objective To review studies on the effect of influenza vaccines on non-influenza respiratory disease (NIRD). Methods Using different search strategies, 18 relevant studies were identified and their strength, limitations and significance were assessed. Results Analysis of 4 RCT datasets did not suggest increased NIRD risk in recipients of live-attenuated vaccines (LAIV) and results of a cohort study suggested short-term protection consistent with the hypothesis of ‘trained immunity’. One RCT, four cohort studies and one test-negative case-control suggested increased NIRD risk in recipients of inactivated influenza vaccines (IIV), whereas five test-negative case-control studies did not show an increased risk associated with a specific viral pathogen. Cross-protection against COVID-19 was suggested in one cross-sectional study on IIV but major biases could not be excluded. Results of four recent ecological studies on COVID-19 were challenging to interpret. Conclusions Available data on LAIV are reassuring but not all those on IIV. A drastic reorientation of 2020–2021 influenza campaigns is probably not warranted but studies aiming to test COVID-19 risk modification among recipients of seasonal influenza vaccines should be planned and funded.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".