This data release details the results of avian influenza sampling of dabbling ducks in Maine and Maryland. These data support an associated USGS publication.

This data layer depicts the relative susceptibility of poultry to the transmission of Avian Influenza from wild birds based upon poultry type and density for the contiguous United States.

Data set containing avian influenza sampling information for late summer and early autumn waterfowl and gulls within and around the Izembek National Wildlife Refuge (NWR), Alaska, 2011-2016. Data contains species, age, sex, collection data and location of sampled migratory birds. Laboratory specific data used to identify presence and absence of influenza A viruses (IAVs) from collected samples are included.

This data set is comprised of four files related to the biosurveillance of low pathogenic avian influenza viruses (LPAIV) in migratory waterfowl at 20 locations in the Delmarva Peninsula in fall/winter of 2013-2014. Two files contain data related to the species, age, and AIV prevalence for all birds sampled (1 data file, 1 definitions file). The other two files contain data related to the primers and standards used in bioassays for AIVs (1 data file, 1 definitions file).

Wild waterfowl (family Anatidae) are reported as secondary transmitters of HPAIV and primary reservoirs for low-pathogenic avian influenza viruses, yet spatial inputs for disease risk modeling for this group have been lacking. Using geographic information software and Monte Carlo simulations, we developed geospatial indices of waterfowl abundance at 1 km resolutions for the breeding and wintering seasons for China, the epicenter of H5N1. Two types of spatial layers were developed: cumulative waterfowl abundance (WAB), a measure of predicted abundance by species, and cumulative abundance weighted by H5N1 prevalence (WPR), whereby abundance for each species was adjusted based on species specific prevalence values. Spatial patterns of the model output differed between seasons, with higher WAB and WPR in the northern and western regions of China for the breeding season and in the southeast for the wintering season. Uncertainty measures indicated highest error in southeastern China for both WAB and WPR.

Datasets used in developing a large volume concentration method to recover avian influenza. These tables include: (1) Information on the solutions used in tests 1 and 2, (2) field parameter at collection and air temperature data corresponding to periods of raw wetland transport and storage, (3) full results of the laboratory studies, (4) results of the ultrafilter test, and (5) standard curve used for calculation of percent recovery.

Emergence of avian influenza viruses with high lethality to humans, such as the currently circulating highly pathogenic A(H5N1) and A(H7N9) cause serious concern for the global economic and public health sectors. To improve our understanding of the spatial and temporal trends in transmission risk we developed a model that predicts hotspots of novel virus emergence between domestic and wild birds and incorporates H5N1 risk factors. Models were produced at 30 km spatial resolution across two temporal seasons. Files are named using a TransmisisonDirection_Season_DataType format such that Transmission direction can end in a (poultry to waterfowl) or b (waterfowl to poultry), Season can be breeding (br) or wintering (wi), and data type can be the mean predicted transmission (mn) or the coefficient of variation (cv).

This data set describes genomic sequence information from 2022 used to infer spatiotemporal trends pertaining to the introductions of highly pathogenic H5N1 avian influenza viruses into Alaska and spread among wild birds, backyard poultry, and mammals.

Influenza A viruses are one of the most significant viral groups globally with substantial impacts on human, domestic animal and wildlife health. Wild birds are the natural reservoirs for these viruses, and active surveillance within wild bird populations provides critical information about viral evolution forming the basis of risk assessments and counter measure development. Unfortunately, active surveillance programs are often resource-intensive, and thus enhancing programs for increased efficiency is paramount. Machine learning, a branch of artificial intelligence applications, provides statistical learning procedures that can be used to gain novel insights into disease surveillance systems. We use a form of machine learning, gradient boosted trees, to estimate the probability of isolating avian influenza viruses (AIV) from wild bird samples collected during surveillance for AIVs from 2006–2011 in the United States. We examined several predictive features including age, sex, bird type, geographic location and matrix gene rrRT-PCR results. Our final model had high predictive power, and only included geographic location and rRT-PCR results as important predictors. The highest predicted viral isolation probability was for samples collected from the north-central states and the south-eastern region of Alaska. Lower rRT-PCR Ct-values are associated with increased likelihood of AIV isolation, and the model estimated 16% probability of isolating AIV from samples declared negative (i.e., ≥ 35 Ct-value) using the rRT-PCR screening test and standard protocols. Our model can be used to prioritize previously collected samples for isolation and rapidly evaluate AIV surveillance designs to maximize the probability of viral isolation given limited resources and laboratory capacity.