Predicting the initial spread of novel Asian origin influenza A viruses in the continental USA by wild waterfowl

Authors

Alan. B. Franklin, APHISWS National Wildlife Research CenterFollow
Sarah N. Bevins, APHISWS National Wildlife Research CenterFollow
Jeremy W. Ellis, APHISWS National Wildlife Research CenterFollow
Ryan S. Miller, APHIS-VS Center for Epidemiology and Animal HealthFollow
Susan A. Shriner, APHISWS National Wildlife Research CenterFollow
J. Jeffrey Root, APHISWS National Wildlife Research CenterFollow
Daniel P. Walsh, National Wildlife Health Center
Thomas J. Deliberto, APHISWS National Wildlife Research CenterFollow

ORCID IDs

http://orcid.org/0000-0001-9491-795X

http://orcid.org/0000-0003-0349-7182

http://orcid.org/0000-0003-4177-1824

Document Type

Article

Date of this Version

11-1-2018

Citation

Franklin, A.B., S.N. Bevins, J.W. Ellis, R.S. Miller, S.A. Shriner, J.J. Root, D.P. Walsh, and T.J. DeLiberto. Predicting the initial spread of novel Asian origin influenza A viruses in the continental United States by wild waterfowl. Transboundary and Emerging Diseases 66(2):705-714. doi: 10.1111/tbed.13070

Comments

U.S. Government work

Abstract

Using data on waterfowl band recoveries, we identified spatially explicit hotspots of concentrated waterfowl movement to predict occurrence and spatial spread of a novel influenza A virus (clade 2.3.4.4) introduced from Asia by waterfowl from an initial outbreak in North America in November 2014. In response to the outbreak, the hotspots of waterfowl movement were used to help guide sampling for clade 2.3.4.4 viruses in waterfowl as an early warning for the US poultry industry during the outbreak. After surveillance sampling of waterfowl, we tested whether there was greater detection of clade 2.3.4.4 viruses inside hotspots. We found that hotspots defined using kernel density estimates of waterfowl band recoveries worked well in predicting areas with higher prevalence of the viruses in waterfowl. This approach exemplifies the value of ecological knowledge in predicting risk to agricultural security.