Continental‐scale dynamics of avian influenza in U.S. waterfowl are driven by demography, migration, and temperature

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.

Spatiotemporal changes in influenza A virus prevalence among wild waterfowl inhabiting the continental United States throughout the annual cycle

“…This information is also useful in helping to inform management practices aiming to mitigate IAV outbreaks in the United States. Specifically, we confirmed the highest IAV prevalence in many dabbling duck species in the autumn, consistent with prior research 15 , 18 – 20 . We also elucidate broad temporal trends in prevalence.…”

“…We found seasonal variation both among and within species. Overall, most species showed an increase in IAV prevalence in the autumn, a pattern that is well established and consistent with past work 15 , 18 – 20 . This pattern is presumably caused by high infection rates among recently fledged, immune-naïve juvenile birds entering the population in late summer and fall 8 , 26 .…”

Influenza A virus surveillance, infection and antibody persistence in snow geese ( Anser caerulescens )

“…Consistent with other broad‐scale evaluations of IAVs in waterfowl in North America (Bevins et al., 2014; Gorsich et al., 2020), we identified variability in infection prevalence across years, sampling month and flyways. However, the correlations identified by our regression model should be interpreted with some caution because the interagency surveillance programme necessarily had a somewhat unbalanced sampling design, partially due to a reliance on opportunistic sampling (e.g.…”

“…The peak median response occurred on 10 dpi and was followed by a gradual decline Because antibody levels for exposed birds dropped below detectable limits by six months post-exposure for most individuals, it is possible that our initial pre-screen may not have identified a prior exposure since the birds were approximately a year old when we acquired them. However, in the event of a previous exposure, we would have expected to see an anamnestic response (a rapid rise in antibodies against a previously encountered pathogen recognized by memory cells), but ELISA results for days 2 and 4 post-inoculation did Consistent with other broad-scale evaluations of IAVs in waterfowl in North America (Bevins et al, 2014;Gorsich et al, 2020), we identified variability in infection prevalence across years, sampling month and flyways. However, the correlations identified by our regression model should be interpreted with some caution because the interagency surveillance programme necessarily had a somewhat unbalanced sampling design, partially due to a reliance on opportunistic sampling (e.g.…”

Transboundary Animal Diseases, an Overview of 17 Diseases with Potential for Global Spread and Serious Consequences

“…LPAI viruses have been identified in a wide range of other birds and mammals, including felines, canines, suids, equines, and mustelids [95][96][97]. The local spread and evolution of LPAI viruses can lead to continental-scale distribution [98]. One LPAI currently circulating worldwide is H9N2, which poses a great risk to small scale and family farms, where it may have great socioeconomic effects [94,[99][100][101].…”