Mitigating risk: Predicting H5N1 avian influenza spread with an empirical model of bird movement
Transboundary and Emerging Diseases
By: Fiona McDuie, Cory T. Overton, Austen Lorenz, Elliott Matchett, Andrea Mott, Desmond Alexander Mackell, Joshua T. Ackerman, Susan E.W. De La Cruz, Vijay P. Patil, Diann Prosser, John Y. Takekawa, Dennis L. Orthmeyer, Maurice E. Pitesky, Samuel L. Diaz-Munoz, Brock M. Riggs, Joseph Gendreau, Eric T. Reed, Mark J. Petrie, Chris K. Williams, Jeffrey J. Buler, Matthew J. Hardy, Brian S. Ladman, Pierre Legagneux, Joel Bety, Philippe J. Thomas, Jean Rodrigue, Josee Lefebvre, and Michael L. Casazza
Links
- More information: Publisher Index Page (via DOI)
- Data Release: USGS data release - Timing of Occurrence of Waterfowl in U.S. Counties and Canadian Counties, Boroughs, Census Districts, and Other Populated Area Designations with Modeled Exposure Status to Highly Pathogenic Avian Influenza Virus in 2021-2022
- Open Access Version: Publisher Index Page
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Abstract
Understanding timing and distribution of virus spread is critical to global commercial and wildlife biosecurity management. A highly pathogenic avian influenza virus (HPAIv) global panzootic, affecting ~600 bird and mammal species globally and over 83 million birds across North America (Dec 2023), poses a serious global threat to animals and public health. We combined a large, long-term waterfowl GPS tracking dataset (16 species) with on-ground disease surveillance data (county-level HPAIv detections) to create a novel empirical model that evaluated spatiotemporal exposure and predicted future spread and potential arrival of HPAIv via GPS tracked migratory waterfowl through 2022. Our model was effective for wild waterfowl, but predictions lagged HPAIv detections in poultry facilities and among some highly impacted non-migratory species. Our results offer critical advance warning for applied biosecurity management and planning and demonstrate the importance and utility of extensive multi-species tracking to highlight potential high-risk disease spread locations and more effectively manage outbreaks.
Suggested Citation
McDuie, F., Overton, C.T., Lorenz, A., Matchett, E., Mott, A., Mackell, D.A., Ackerman, J.T., De La Cruz, S.E., Patil, V.P., Prosser, D., Takekawa, J., Orthmeyer, D.L., Pitesky, M.E., Diaz-Munoz, S.L., Riggs, B.M., Gendreau, J., Reed, E.T., Petrie, M.J., Williams, C.K., Buler, J.J., Hardy, M., Ladman, B.S., Legagneux, P., Bety, J., Thomas, P.J., Rodrigue, J., Lefebvre, J., and Casazza, M.L., 2024, Mitigating risk: Predicting H5N1 avian influenza spread with an empirical model of bird movement: Transboundary and Emerging Diseases, 5525298, 15 p.; Data Release, https://doi.org/10.1155/2024/5525298.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Mitigating risk: Predicting H5N1 avian influenza spread with an empirical model of bird movement |
| Series title | Transboundary and Emerging Diseases |
| DOI | 10.1155/2024/5525298 |
| Publication Date | July 18, 2024 |
| Year Published | 2024 |
| Language | English |
| Publisher | Wiley |
| Contributing office(s) | Western Ecological Research Center |
| Description | 5525298, 15 p.; Data Release |