ArticleScientific reports2025
Risk factors for highly pathogenic avian influenza outbreaks in Japan during 2022-2023 season identified by additive Bayesian network modeling.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Review
- Influenza A Virus H5N1 Subtype: Resurgent Interspecies and Intercontinental Transmission, and a New Host.Pathogens (Basel, Switzerland) · 2025Review
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Authors and funding
4 authors.
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Abstract
Highly pathogenic avian influenza (HPAI) has caused significant damage to the poultry industry globally, including in Japan. To identify farm-level risk factors for HPAI infection while considering potential confounding and correlations among variables, an additive Bayesian network (ABN) model was applied. This case-control study analyzed outbreaks in layer and broiler farms during the 2022-2023 HPAI season in Japan, selecting 69 infected farms as cases and 361 uninfected farms located within a 5-km radius as controls. The ABN model incorporated four variables: HPAI infection status, flock size, production type, and coverage of surrounding water bodies. Results indicated that layer farms, farms with large flock sizes, and those situated near extensive water bodies faced a higher risk of HPAI infection. Notably, being a layer farm increased risk both directly and indirectly, due to their tendency to maintain larger flocks. These findings highlight the importance of reinforcing biosecurity measures on farms with these characteristics to prevent future HPAI outbreaks.
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