tetano
Editor, Senior Moderator
One Health
. 2026 Jun 13:23:101483.
doi: 10.1016/j.onehlt.2026.101483. eCollection 2026 Dec.
Spatiotemporal distribution trends and driving factors of avian influenza in poultry in China
Feifei Li[SUP] 1 2 [/SUP], Linsheng Yang[SUP] 1 2 [/SUP], Hairong Li[SUP] 1 2 [/SUP], Li Wang[SUP] 1 2 [/SUP], Lijuan Gu[SUP] 1 2 [/SUP], Svetlana Malkhazova[SUP] 3 [/SUP]
Affiliations
Background: Avian influenza viruses can cross species barriers to mammals, spreading among wildlife, domestic animals and humans. Poultry, as a primary host, plays a crucial role in the transmission of avian influenza.
Methods: This study analyzed the temporal patterns and hotspot regions of avian influenza in poultry, examined the distribution trends of major subtypes, identified driving factors on the avian influenza transmission, and assessed their contributions and interactive effect using the XGBoost model.
Results: Poultry avian influenza in China mainly occurred from November to March, with hotspot areas concentrated in southeastern China. The H5 subtype persisted throughout the study period, and its epidemic center gradually shifted eastward. The H7 and H9 subtypes showed different temporal trends in circulation, while H9N2 accounted for the highest number of reported cases. Human activity-related variables, including road network density, population density, GDP and poultry trade points, contributed the most to poultry avian influenza outbreaks. Climate factors ranked the second, with winter precipitation contributing the most. Livestock and poultry farming density ranked the third and waterfowl species density ranked the last. The increase of human activities, livestock and farming densities contributed to a sharp increase in avian influenza risks when the levels of these factors were at low-medium level, while the contribution became stable when the human activities exceeded to certain threshold, and became negative when the livestock and farming densities were extremely high. Further, poultry avian influenza risk was amplified under the combined effects of livestock and poultry farming systems, high waterfowl species density, and strong trade connectivity, suggesting a coupled wild bird-poultry-trade network transmission pattern.
Conclusion: These findings highlight that socioeconomic activities are the major contributors for poultry avian influenza, while climate and waterfowl species density acted as important modifying factors for avian influenza, in particular with livestock and poultry farming density. The study provides a useful framework for identifying potential transmission interfaces and supports more targeted surveillance and prevention strategies under the One Health perspective.
Keywords: Avian influenza; Environmental factors; Poultry; Spatiotemporal analysis; XGBoost model.
. 2026 Jun 13:23:101483.
doi: 10.1016/j.onehlt.2026.101483. eCollection 2026 Dec.
Spatiotemporal distribution trends and driving factors of avian influenza in poultry in China
Feifei Li[SUP] 1 2 [/SUP], Linsheng Yang[SUP] 1 2 [/SUP], Hairong Li[SUP] 1 2 [/SUP], Li Wang[SUP] 1 2 [/SUP], Lijuan Gu[SUP] 1 2 [/SUP], Svetlana Malkhazova[SUP] 3 [/SUP]
Affiliations
- PMID: 42376211
- PMCID: PMC13311913
- DOI: 10.1016/j.onehlt.2026.101483
Background: Avian influenza viruses can cross species barriers to mammals, spreading among wildlife, domestic animals and humans. Poultry, as a primary host, plays a crucial role in the transmission of avian influenza.
Methods: This study analyzed the temporal patterns and hotspot regions of avian influenza in poultry, examined the distribution trends of major subtypes, identified driving factors on the avian influenza transmission, and assessed their contributions and interactive effect using the XGBoost model.
Results: Poultry avian influenza in China mainly occurred from November to March, with hotspot areas concentrated in southeastern China. The H5 subtype persisted throughout the study period, and its epidemic center gradually shifted eastward. The H7 and H9 subtypes showed different temporal trends in circulation, while H9N2 accounted for the highest number of reported cases. Human activity-related variables, including road network density, population density, GDP and poultry trade points, contributed the most to poultry avian influenza outbreaks. Climate factors ranked the second, with winter precipitation contributing the most. Livestock and poultry farming density ranked the third and waterfowl species density ranked the last. The increase of human activities, livestock and farming densities contributed to a sharp increase in avian influenza risks when the levels of these factors were at low-medium level, while the contribution became stable when the human activities exceeded to certain threshold, and became negative when the livestock and farming densities were extremely high. Further, poultry avian influenza risk was amplified under the combined effects of livestock and poultry farming systems, high waterfowl species density, and strong trade connectivity, suggesting a coupled wild bird-poultry-trade network transmission pattern.
Conclusion: These findings highlight that socioeconomic activities are the major contributors for poultry avian influenza, while climate and waterfowl species density acted as important modifying factors for avian influenza, in particular with livestock and poultry farming density. The study provides a useful framework for identifying potential transmission interfaces and supports more targeted surveillance and prevention strategies under the One Health perspective.
Keywords: Avian influenza; Environmental factors; Poultry; Spatiotemporal analysis; XGBoost model.