These data describe the results of virus isolation from oropharyngeal/cloacal swabs and testing of sera by a commercial blocking enzyme-linked immunosorbent assay (bELISA) and hemagglutinin (HA) specific testing by microneutralization (MN) and hemagglutination inhibition (HI) for Lesser Scaup in both the Atlantic and Pacific Flyways and Greater Scaup in the Pacific Flyway. These data support a USGS published manuscript.

Data set containing avian influenza A virus (IAV) sampling information for Emperor Geese in Alaska, 2015-2017. The data are in three tables: 1) collection data and IAV screening results from fecal samples at several sites in southwestern Alaska, 2) results of blocking enzyme-linked immunosorbent assay (bELISA) tests for IAV antibodies in blood serum collected from nesting female Emperor geese near the Manokinak River on the Yukon-Kuskokwim River Delta, and 3) results of influenza virus microneutralization assays to test for the specific IAV subtypes in the Manokinak samples.

Data set containing avian influenza A virus (IAV) sampling information for Emperor Geese in Alaska, 2015-2017. The data are in three tables: 1) collection data and IAV screening results from fecal samples at several sites in southwestern Alaska, 2) results of blocking enzyme-linked immunosorbent assay (bELISA) tests for IAV antibodies in blood serum collected from nesting female Emperor geese near the Manokinak River on the Yukon-Kuskokwim River Delta, and 3) results of influenza virus microneutralization assays to test for the specific IAV subtypes in the Manokinak samples.

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.

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.

Dataset containing avian influenza screening results for waterfowl and gulls sampled during autumn in (or near) Izembek National Wildlife Refuge (NWR), Alaska, 2011-2024. These data contain information on species, age, and sex of birds sampled, collection dates, and laboratory testing information used to determine the presence and absence of influenza A viruses (IAVs).

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.

These data describe avian influenza sampling efforts for eastern wild turkeys (Meleagris gallopavo silvestris) across the Maryland portion of the Delmarva Peninsula, USA in the winter of 2023-2024

These data describe avian influenza sampling efforts for eastern wild turkeys (Meleagris gallopavo silvestris) across the Maryland portion of the Delmarva Peninsula, USA in the winter of 2023-2024

A compilation of previously published results exploring the pathogenesis of avian influenza viruses in wild avian species in North America.