The Impact of Migratory Flyways on the Spread of Avian Influenza Virus in North America
Bibliographic record
Abstract
Abstract Wild birds are the major reservoir hosts for influenza A viruses (AIVs) and have been implicated in the emergence of pandemic events in livestock and human populations. Understanding how AIVs spread within and across continents is therefore critical to the development of successful strategies to manage and reduce the impact of influenza outbreaks. In North America many bird species undergo seasonal migratory movements along a North-South axis, thereby fostering opportunities for viruses to spread over long distances. However, the role played by such avian flyways in shaping the genetic structure of AIV populations has proven controversial. To assess the relative contribution of bird migration along flyways to the genetic structure of AIV we performed a large-scale phylogeographic study of viruses sampled in the USA and Canada, involving the analysis of 3805 to 4505 sequences from 36 to 38 geographic localities depending on the gene data set. To assist this we developed a maximum likelihood-based genetic algorithm to explore a wide range of complex spatial models, thereby depicting a more complete picture of the migration network than previous studies. Based on phylogenies estimated from nucleotide data sets, our results show that AIV migration rates within flyways are significantly higher than those between flyways, indicating that the migratory patterns of birds play a key role in pathogen dispersal. These findings provide valuable insights into the evolution, maintenance and transmission of AIVs, in turn allowing the development of improved programs for surveillance and risk assessment. Significance Statement Avian influenza viruses infect a wide variety of wild bird species and represent a potential disease threat to the poultry industry and hence to human and livestock populations. However, the ecological factors that drive the geographic spread and evolution of these viruses are both poorly understood and controversial at the continental scale, particularly the role played by migratory flyways in shaping patterns of virus dispersal. Using a novel phylogeographic analysis of large genomic data sets we show migration flyways act as important transmission barriers to the spread of avian influenza viruses in North America. Hence, these results indicate that the spread of avian influenza virus in wild birds in North America has an element of predictability.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".