Data mining and model-predicting a global disease reservoir for low-pathogenic Avian Influenza (AI) in the wider pacific rim using big data sets

• Published in: Scientific Reports Vol. 10; no. 1; p. 16817
• Main Authors: Gulyaeva, Marina, Huettmann, Falk, Shestopalov, Alexander, Okamatsu, Masatoshi, Matsuno, Keita, Chu, Duc-Huy, Sakoda, Yoshihiro, Glushchenko, Alexandra, Milton, Elaina, Bortz, Eric
• Format: Journal Article
• Language: English
• Published: London Springer Science and Business Media LLC 08.10.2020; Nature Publishing Group UK; Nature Publishing Group
• Subjects: 631/114; 631/158; 631/1647; 631/326; 631/601; 631/61; 639/705; 692/308; 692/499; 692/699; 692/700; 704/106; 704/158; 704/172; 704/4111; 704/47; 704/844; Animals; Aquatic birds; Avian flu; Big Data; Birds; Birds - virology; Chickens; Chickens - virology; Data Mining; Data Mining - methods; Datasets as Topic; Disease Reservoirs; Disease Reservoirs - statistics & numerical data; Disease Reservoirs - virology; Ducks; Ducks - virology; Forecasting; Geographic information systems; Humanities and Social Sciences; Immune system; Influenza; Influenza in Birds; Influenza in Birds - epidemiology; Influenza in Birds - virology; Learning algorithms; Machine learning; Models, Statistical; multidisciplinary; Niches; Orthomyxoviridae; Orthomyxoviridae - pathogenicity; Pacific Ocean; Pandemics; Prevalence; Remote sensing; Science; Science (multidisciplinary); Wildlife; Pacific Ocean

Avian Influenza (AI) is a complex but still poorly understood disease; specifically when it comes to reservoirs, co-infections, connectedness and wider landscape perspectives. Low pathogenic (Low-path LP) AI in chickens caused by less virulent strains of AI viruses (AIVs)—when compared with highly p...