Continental‐scale dynamics of avian influenza in U.S. waterfowl are driven by demography, migration, and temperature. Issue 2 (22nd November 2020)

• Record Type: Journal Article
• Authors: Gorsich, Erin E.; Webb, Colleen T.; Merton, Andrew A.; Hoeting, Jennifer A.; Miller, Ryan S.; Farnsworth, Matthew L.; Swafford, Seth R.; DeLiberto, Thomas J.; Pedersen, Kerri; Franklin, Alan B.; McLean, Robert G.; Wilson, Kenneth R.; Doherty, Paul F.
• Is Part Of: Ecological applications. Volume 31:Issue 2(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-22
• Subjects: avian influenza -- bird migration -- contact structure -- influenza A virus -- multi‐scale analysis -- network; Ecology -- Periodicals; Environmental protection -- Periodicals; Biology, Economic -- Periodicals; 577.05
• DOI: 10.1002/eap.2245
• Languages: English
• ISSNs: 1051-0761

Abstract

Abstract: Emerging diseases of wildlife origin are increasingly spilling over into humans and domestic animals. Surveillance and risk assessments for transmission between these populations are informed by a mechanistic understanding of the pathogens in wildlife reservoirs. For avian influenza viruses (AIV), much observational and experimental work in wildlife has been conducted at local scales, yet fully understanding their spread and distribution requires assessing the mechanisms acting at both local, (e.g., intrinsic epidemic dynamics), and continental scales, (e.g., long‐distance migration). Here, we combined a large, continental‐scale data set on low pathogenic, Type A AIV in the United States with a novel network‐based application of bird banding/recovery data to investigate the migration‐based drivers of AIV and their relative importance compared to well‐characterized local drivers (e.g., demography, environmental persistence). We compared among regression models reflecting hypothesized ecological processes and evaluated their ability to predict AIV in space and time using within and out‐of‐sample validation. We found that predictors of AIV were associated with multiple mechanisms at local and continental scales. Hypotheses characterizing local epidemic dynamics were strongly supported, with age, the age‐specific aggregation of migratory birds in an area and temperature being the best predictors of infection. Hypotheses defining larger, network‐based features of the migration processes, such as clustering or between‐cluster mixing explained less variation but were also supported. Therefore, our results support a role for local processes in driving the continental distribution of AIV.