Understanding how these processes interact and scale is essential to designing pathogen‐specific surveillance and control strategies because it allows key processes driving infection to be targeted.Īvian influenza viruses (AIV) are multi‐host, viral infections with a global distribution and a complex, multi‐scale transmission ecology (Olsen et al. Alternatively, larger‐scale processes such as climate variability or seasonal movements can also drive disease dynamics (Wesolowski et al.
For example, the seasonal aggregation of school children or urban workers is thought to be a dominant driver of directly transmitted, immunizing infections, such as measles (Metcalf et al. Processes acting at local scales may predominate and scale up to influence disease at larger scales. A fundamental challenge in characterizing these mechanisms is quantifying the relative importance of multiple processes acting across spatial scales (Plowright et al. Surveillance and risk assessments for emerging diseases of wildlife origin are informed by a mechanistic understanding of their spread and distribution in wildlife reservoirs (Cunningham et al. Therefore, our results support a role for local processes in driving the continental distribution of AIV. Hypotheses defining larger, network‐based features of the migration processes, such as clustering or between‐cluster mixing explained less variation but were also supported. 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. We found that predictors of AIV were associated with multiple mechanisms at local and continental scales. 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.
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). 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). Surveillance and risk assessments for transmission between these populations are informed by a mechanistic understanding of the pathogens in wildlife reservoirs. Emerging diseases of wildlife origin are increasingly spilling over into humans and domestic animals.