tetano
Editor, Senior Moderator
Sci Rep
. 2025 Jul 23;15(1):26739.
doi: 10.1038/s41598-025-13003-5. Risk factors for highly pathogenic avian influenza outbreaks in Japan during 2022-2023 season identified by additive Bayesian network modeling
Emi Yamaguchi[SUP] 1 [/SUP], Yoko Hayama[SUP] 1 [/SUP], Sonoko Kondo[SUP] 1 [/SUP], Takehisa Yamamoto[SUP] 2 [/SUP]
Affiliations
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.
Keywords: Additive Bayesian network; Highly pathogenic avian influenza.
. 2025 Jul 23;15(1):26739.
doi: 10.1038/s41598-025-13003-5. Risk factors for highly pathogenic avian influenza outbreaks in Japan during 2022-2023 season identified by additive Bayesian network modeling
Emi Yamaguchi[SUP] 1 [/SUP], Yoko Hayama[SUP] 1 [/SUP], Sonoko Kondo[SUP] 1 [/SUP], Takehisa Yamamoto[SUP] 2 [/SUP]
Affiliations
- PMID: 40702212
- PMCID: PMC12287441
- DOI: 10.1038/s41598-025-13003-5
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.
Keywords: Additive Bayesian network; Highly pathogenic avian influenza.