Predicting the initial spread of novel Asian origin influenza A viruses in the continental USA by wild waterfowl

Integrating Citizen Scientist Data into the Surveillance System for Avian Influenza Virus, Taiwan

The continuing circulation and reassortment with low-pathogenicity avian influenza Gs/Gd (goose/Guangdong/1996)-like avian influenza viruses (AIVs) has caused huge economic losses and raised public health concerns over the zoonotic potential. Virologic surveillance of wild birds has been suggested as part of a global AIV surveillance system. However, underreporting and biased selection of sampling sites has rendered gaining information about the transmission and evolution of highly pathogenic AIV problematic. We explored the use of the Citizen Scientist eBird database to elucidate the dynamic distribution of wild birds in Taiwan and their potential for AIV exchange with domestic poultry. Through the 2-stage analytical framework, we associated nonignorable risk with 10 species of wild birds with >100 significant positive results. We generated a risk map, which served as the guide for highly pathogenic AIV surveillance. Our methodologic blueprint has the potential to be incorporated into the global AIV surveillance system of wild birds.

Intercontinental Movement of Highly Pathogenic Avian Influenza A(H5N1) Clade 2.3.4.4 Virus to the United States, 2021

We detected Eurasian-origin highly pathogenic avian influenza A(H5N1) virus belonging to the Gs/GD lineage, clade 2.3.4.4b, in wild waterfowl in 2 Atlantic coastal states in the United States. Bird banding data showed widespread movement of waterfowl within the Atlantic Flyway and between neighboring flyways and northern breeding grounds.

Surveillance system for avian influenza in wild birds and implications of its improvement with insights into the highly pathogenic avian influenza outbreaks in Japan

Since the re-emergence of a highly pathogenic avian influenza (HPAI) in 2004, outbreaks of the viral subtypes HPAI, H5N1, H5N8, and H5N6 in wild birds, poultry, and zoo birds have occurred in Japan. In 2008, a nation-wide avian influenza (AI) surveillance program was started for the early detection of the HPAI virus (HPAIV) and for the assessment of HPAIV infection among wild birds. In this study, we aimed to conduct an overview of the AI surveillance system of wild birds in Japan, including those in the regions and prefectures, to assess its overall performance and develop insights on its improvement. We analyzed past surveillance data in Japan and conducted questionnaire surveys for the officers in 11 regional branches of the Ministry of Environment and the nature conservation divisions of 47 prefectures to acquire details regarding those AI surveillance. We found that the early detection of HPAIV in wild birds was successfully achieved in only one of the five outbreak seasons during the 2008-2019 period in Japan, and the assessment of HPAIV infection had possibly not been adequate in the national surveillance system. In the winter season, AI surveillance in most prefectures were mainly conducted by means of passive surveillance through reported dead birds and active surveillance through collected waterbird feces. Conversely, less than half of the prefectures conducted bird monitoring, patrolling in migratory bird habitats, and AI antigen testing in rescued birds. In areas surrounding HPAI occurrence sites (<10 km), bird monitoring and patrolling efforts were enhanced. However, AI testing efforts in waterbird feces and rescued birds were decreased. The AI surveillance for endangered bird species and in national wildlife protection areas was conducted by the branches of the Ministry of Environment and by the prefectures. Based on our results, we concluded that for maximum efficiency, legislation which specialized in wildlife pathogens should be necessary to prepare adequate national budget and testing capacity for appropriate surveillance system with periodical assessment for surveillance results and the system.

Artificial intelligence and avian influenza: Using machine learning to enhance active surveillance for avian influenza viruses

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 countermeasure 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 to 2011 in the United States. We examined several predictive features including age, sex, bird type, geographic location and matrix gene rRT-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.