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Lightweight multi-scale early warning system for influenza A spillovers

Alfonsi, T.; Bernasconi, A.; Chiara, M.; Ceri, S.

2025-09-24 bioinformatics
10.1101/2025.05.24.655955 bioRxiv
Show abstract

Spillovers of Influenza A viruses into farmed animals and humans have the potential to trigger epidemics or even global pandemics. We introduce FluWarning, a highly efficient and elegant computational method based on anomaly detection of codon bias and dinucleotide composition for early flagging of divergent viral HA segments. Applied to H1N1 specimens collected between 2008-2009, FluWarning accurately identifies the emergence of pdm09 - the virus that caused the 2009 flu pandemic; warnings precede the observed progression of the pandemic. Applied to H5N1 specimens collected between 2019 and 2025, FluWarning flagged the emergence of the B3.13 genotype, linked to a spillover event in dairy cows in the United States. In summary, FluWarning is an effective multi-scale warning system for IAVs, detecting spillovers with few available sequences. One-Sentence SummaryEarly detection of zoonoses using a few viral sequences is critical for the timely implementation of mitigation strategies.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
Nature Communications
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Top 9%
18.5%
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2
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7.9%
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Top 5%
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8
iScience
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Top 7%
3.2%
50% of probability mass above
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Top 5%
3.2%
10
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○ 46 papers in training set
Top 0.2%
2.8%
11
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Top 2%
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12
eLife
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13
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○ 183 papers in training set
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2.4%
14
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Top 12%
1.9%
15
Genome Biology
○ 637 papers in training set
Top 6%
1.7%
16
Scientific Reports
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Top 54%
1.7%
17
Cell
○ 431 papers in training set
Top 7%
1.4%
18
Advanced Science
○ 286 papers in training set
Top 6%
1.3%
19
PLOS Biology
● 486 papers in training set
Top 7%
1.1%
20
Genome Research
● 468 papers in training set
Top 5%
1.1%
21
Science
● 477 papers in training set
Top 7%
1.1%
22
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○ 201 papers in training set
Top 4%
1.1%
23
Molecular Biology and Evolution
◐ 542 papers in training set
Top 4%
1.1%
24
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○ 113 papers in training set
Top 3%
1.0%
25
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1.0%
26
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1.0%
27
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Top 10%
1.0%
28
Molecular Systems Biology
○ 162 papers in training set
Top 3%
1.0%
29
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Top 10%
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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.