Current status and future challenges of avian influenza – a literature review
Bibliographic record
Abstract
An infectious agent affecting both domestic and wild birds may cause avian influenza. All of them can be transmitted by coming into contact with tainted food, drink, or bird emissions, particularly feces. Numerous clades of H5N1 infections have been circulating since 2003, including one introduced to the United States in 2014 by wild birds, which persisted until 2016. There were 2,240 wild birds found in 45 states and 519 counties in the United States alone by September 14, 2022. According to the World Organization for Animal Health (WOAH), the predominant Highly Pathogenic Avian Influenza (HPAI) A (H5) virus subtype causing poultry outbreaks worldwide from late 2021 to early 2022 is A (H5N1). Most notifications from wild birds across multiple countries and regions suggest that the virus may have been introduced and spread via uncontrolled bird migration. The primary instance of a goose/Guangdong/1/96-lineage H5 HPAI infection inside the Americas since June 2015 was checked by the later disclosure of an H5N1 HPAI outbreak in Newfoundland, Canada. The avian flu Type A viruses, or bird flu viruses, rarely cause human infection; some bird flu viruses have done so in the past. The HPAI (H5) virus has been persistent in wild bird populations in Europe since the 2020-21 epidemic wave, according to the paper titled “Avian Influenza Overview: March-June 2022.” Even regions like Antarctica had avian influenza cases in 2023-24. Prevention and control can be done by monitoring and reporting outbreaks, preventing avian influenza at its source in animals, banning chicken farms, controlling methodologies, remuneration for ranchers, and vaccination.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".