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.

,Changes in influenza viruses that infect pigs have been observed for over two decades. The evolution of these viruses has led to several new strains. For instance, one such strain known as H1N2 emerged to infect pigs in the United States in 2015. This virus recently gained prevalence after acquiring a new N1 gene. Our study aimed to determine if the new N1 gene was responsible for increased transmission of the virus among pigs. To investigate this hypothesis, we analyzed four different strains of H1N1 or H1N2 influenza A virus. Each strain had different combinations of N1 and N2 genes. We infected pigs with these viruses and observed their clinical signs as well as transmission to other pigs. Regardless of the viral genes present, all variants of the virus were transmitted from one infected pig to another at the same levels. Therefore, the new N1 gene did not seem to have direct importance in increasing the transmission of the virus. These results suggest that the increase in detection may be associated with less protection from previous vaccines or infections due to the change in N1 or N2, or changes related to pig management or movement. Understanding how influenza A viruses spread provides important insights for the swine industry for disease prevention and vaccine developers for vaccine strain selection.,

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.

구제역 혈청예찰 월별 분석자료 및 국내 발생 구제역 바이러스에 대한 유전자 정보 등을 제공함으로써 구제역과 관련된 연구자료 등으로 활용

2022년 질병관리청 및 협력병원과 연계하여 경상남도 인플루엔자 및 호흡기감염증의 원인병원체를 분석한 결과를 제공합니다.

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.

야생동물질병관리시스템은 철새도래지, 강변 등을 중심으로 분변예찰을 수행합니다. 지방자치단체, 용역업체, 국립야생동물질병관리원 자체예찰 등으로 수집된 시료들을 야생동물질볃관리시스템을 통해 질병진단의뢰하며 이를 국립야생동물질병관리원이 검사하여 질병데이터를 수집합니다. "AI 예찰정보"는 각 시료에 부여된 번호에 따라 채취에 관한 정보(채취기관, 채취일자)와 질병검사에 관한 정보(선별검사확인일자, 양성·음성 등 병원성결과, 유전자형)와 같이 각종 정보가 제공됩니다.유전자형에 공란이 많은 이유는 고병원성·저병원성 양성결과에만 유전자형이 기록되며, 그 외 음성 등은 유전자형이 기록되지 않기 때문입니다.

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.