Famke Alberts ^1* Olaf Berke ^1,2,3 Leilani Rocha ¹ Sheila Keay ¹ Grazieli Maboni ^4,5 Zvonimir Poljak ^1,2

• ¹ Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Canada
• ² Center for Public Health and Zoonoses, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
• ³ Centre for Advancing Responsible & Ethical Artificial Intelligence, University of Guelph, Guelph, Canada
• ⁴ Department of Pathobiology, Ontario Veterinary College, University of Guelph, Ontario, Ontario, Canada
• ⁵ Department of Population Health, College of Veterinary Medicine, University of Georgia, Athens, Georgia, United States

Predicting which species are susceptible to viruses (i.e., host range) is important for understanding and developing effective strategies to control viral outbreaks in both humans and animals. The use of machine learning and bioinformatic approaches to predict viral hosts has been expanded with advancements in in-silico techniques. We conducted a scoping review to identify the breadth of machine learning methods applied to influenza and coronavirus genome data for the identification of susceptible host species. The protocol for this scoping review is available at https://hdl.handle.net/10214/26112. Five online databases were searched, and 1217 citations, published between January 2000 and May 2022, were obtained, and screened in duplicate for English language and in-silico research, covering the use of machine learning to identify susceptible species to viruses. Fifty-three relevant publications were identified for data charting. The breadth of research was extensive including 32 different machine learning algorithms used in combination with 29 different feature selection methods and 43 different genome data input formats. There were 20 different methods used by authors to assess accuracy. Authors mostly used influenza viruses (n = 31/53 publications, 58.5%), however, more recent publications focused on coronaviruses and other viruses in combination with influenza viruses (n = 22/53, 41.5%). The susceptible animal groups authors most used were humans (n = 57/77 analyses, 74.0%), avian (n = 35/77 45.4%), and swine (n = 28/77, 36.4%). In total, 53 different hosts were used and, in most publications, data from multiple hosts was used. The main gaps in research were a lack of standardized reporting of methodology and the use of broad host categories for classification. Overall, approaches to viral host identification using machine learning were diverse and extensive.