Evolutionary dynamics of the clade 2.3.4.4B H5N8 high‐pathogenicity avian influenza outbreaks in coastal seabirds and other species in southern Africa from 2017 to 2019
Bibliographic record
Abstract
From late 2017 to early 2018, clade 2.3.4.4B H5N8 highly pathogenic avian influenza (HPAI) viruses caused mass die-offs of thousands of coastal seabirds along the southern coastline of South Africa. Terns (Laridae) especially were affected, but high mortalities in critically endangered and threatened species like African Penguins (Spheniscus demersus) caused international concern and, exactly a year later, the disease recurred at a key African Penguin breeding site on Halifax Island, Namibia. Twenty-five clade 2.3.4.4B H5N8 HPAI viruses from coastal seabirds and a Jackal Buzzard (Buteo rufofuscus) were isolated and/or sequenced in this study. Phylogenetic analyses of the full viral genomes and time to the most recent common ancestor (tMRCA) analyses of the HA, NA, PB1 and PA genes determined that the South African coastal seabird viruses formed a monophyletic group nested within the South African genotype 4 viruses. This sub-lineage likely originated from a single introduction by terrestrial birds around October 2017. Only the HA and NA sequences were available for the Namibian penguin viruses, but the phylogenetic data confirmed that the South African coastal seabird viruses from 2017 to 2018 were the source and the most closely related South African virus was found in a gull. tMRCA analyses furthermore determined that the progenitors of the five genotypes implicated in the earlier 2017 South African outbreaks in wild birds and poultry were dated at between 2 and 4 months prior to the index cases. tMRCA and phylogenetic data also showed that the novel genotype 6 virus introduced to South Africa in 2018, and later also detected in Nigeria and Poland in 2019, most likely arose in late 2017 in West, Central or East Africa. We propose that it continued to circulate there, and that an unidentified reservoir was the source of both the South African outbreaks in early 2018 and in Nigeria in mid-2019.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".