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Article

Perpetuation of Avian Influenza from Molt to Fall Migration in Wild Swan Geese (Anser cygnoides): An Agent-Based Modeling Approach

by
John Y. Takekawa
1,2,*,†,
Chang-Yong Choi
1,2,3,
Diann J. Prosser
4,
Jeffery D. Sullivan
4,
Nyambayar Batbayar
5 and
Xiangming Xiao
2
1
U.S. Geological Survey, Western Ecological Research Center, Vallejo, CA 94592, USA
2
School of Biological Sciences, University of Oklahoma, Norman, OK 73019, USA
3
Department of Forest Sciences, Seoul National University, Seoul 08826, Republic of Korea
4
U.S. Geological Survey, Eastern Ecological Science Center, Laurel, MD 20708, USA
5
Wildlife Science and Conservation Center, Ulaanbaatar 210351, Mongolia
*
Author to whom correspondence should be addressed.
Current address: Suisun Resource Conservation District, Suisun City, CA 94585, USA.
Viruses 2025, 17(2), 196; https://doi.org/10.3390/v17020196
Submission received: 27 September 2024 / Revised: 13 January 2025 / Accepted: 24 January 2025 / Published: 30 January 2025

Abstract

Wild waterfowl are considered to be the reservoir of avian influenza, but their distinct annual life cycle stages and their contribution to disease dynamics are not well understood. Studies of the highly pathogenic avian influenza (HPAI) virus have primarily focused on wintering grounds, where human and poultry densities are high year-round, compared with breeding grounds, where migratory waterfowl are more isolated. Few if any studies of avian influenza have focused on the molting stage where wild waterfowl congregate in a few selected wetlands and undergo the simultaneous molt of wing and tail feathers during a vulnerable flightless period. The molting stage may be one of the most important periods for the perpetuation of the disease in waterfowl, since during this stage, immunologically naïve young birds and adults freely intermix prior to the fall migration. Our study incorporated empirical data from virological field samplings and markings of Swan Geese (Anser cygnoides) on their breeding grounds in Mongolia in an integrated agent-based model (ABM) that included susceptible–exposed–infectious–recovered (SEIR) states. Our ABM results provided unique insights and indicated that individual movements between different molting wetlands and the transmission rate were the key predictors of HPAI perpetuation. While wetland extent was not a significant predictor of HPAI perpetuation, it had a large effect on the number of infections and associated death toll. Our results indicate that conserving undisturbed habitats for wild waterfowl during the molting stage of the breeding season could reduce the risk of HPAI transmission.
Keywords: highly pathogenic avian influenza; HPAI; movement ecology; agent-based model; SEIR model; susceptible–exposed–infectious–recovered; swan goose; telemetry highly pathogenic avian influenza; HPAI; movement ecology; agent-based model; SEIR model; susceptible–exposed–infectious–recovered; swan goose; telemetry

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MDPI and ACS Style

Takekawa, J.Y.; Choi, C.-Y.; Prosser, D.J.; Sullivan, J.D.; Batbayar, N.; Xiao, X. Perpetuation of Avian Influenza from Molt to Fall Migration in Wild Swan Geese (Anser cygnoides): An Agent-Based Modeling Approach. Viruses 2025, 17, 196. https://doi.org/10.3390/v17020196

AMA Style

Takekawa JY, Choi C-Y, Prosser DJ, Sullivan JD, Batbayar N, Xiao X. Perpetuation of Avian Influenza from Molt to Fall Migration in Wild Swan Geese (Anser cygnoides): An Agent-Based Modeling Approach. Viruses. 2025; 17(2):196. https://doi.org/10.3390/v17020196

Chicago/Turabian Style

Takekawa, John Y., Chang-Yong Choi, Diann J. Prosser, Jeffery D. Sullivan, Nyambayar Batbayar, and Xiangming Xiao. 2025. "Perpetuation of Avian Influenza from Molt to Fall Migration in Wild Swan Geese (Anser cygnoides): An Agent-Based Modeling Approach" Viruses 17, no. 2: 196. https://doi.org/10.3390/v17020196

APA Style

Takekawa, J. Y., Choi, C.-Y., Prosser, D. J., Sullivan, J. D., Batbayar, N., & Xiao, X. (2025). Perpetuation of Avian Influenza from Molt to Fall Migration in Wild Swan Geese (Anser cygnoides): An Agent-Based Modeling Approach. Viruses, 17(2), 196. https://doi.org/10.3390/v17020196

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