Reassortants of the Highly Pathogenic Influenza Virus A/H5N1 Causing Mass Swan Mortality in Kazakhstan from 2023 to 2024
Bibliographic record
Abstract
In the winter of 2023/2024, the mass death of swans was observed on Lake Karakol on the eastern coast of the Caspian Sea. From 21 December 2023 to 25 January 2024, 1132 swan corpses (Cygnus olor, Cygnus cygnus) were collected and disposed of on the coast by veterinary services and ecologists. Biological samples were collected from 18 birds for analysis at different dates of the epizootic. It was found that the influenza outbreak was associated with a high concentration of migrating birds at Lake Karakol as a result of a sharp cold snap in the northern regions. At different dates of the epizootic, three avian influenza A/H5N1 viruses of clade 2.3.4.4.b were isolated from dead birds and identified as highly pathogenic viruses (HPAIs) based on the amino acid sequence of the hemagglutinin multi-base proteolytic cleavage site (PLREKRRRKR/G). A phylogenetic analysis showed that the viruses isolated from the swans had reassortations in the PB2, PB1, and NP genes between highly pathogenic (HP) and low-pathogenic (LP) avian influenza viruses. Avian influenza viruses A/Cygnus cygnus/Karakol lake/01/2024(H5N1) and A/Mute swan/Karakol lake/02/2024(H5N1) isolated on 10 January 2024 received PB2, PB1, and NP from LPAIV, while A/Mute swan/Mangystau/9809/2023(H5N1) isolated on 26 December 2023 received PB1 and NP from LPAIV, indicating that the H5N1 viruses in this study are new reassortants. All viruses showed amino acid substitutions in the PB2, PB1, NP, and NS1 segments, which are critical for enhanced virulence or adaptation in mammals. An analysis of the genomes of the isolated viruses showed that bird deaths during different periods of the epizootic were caused by different reassortant viruses. Kazakhstan is located at the crossroads of several migratory routes of migratory birds, and the possible participation of wild birds in the introduction of various pathogens into the regions of Kazakhstan requires further study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".