Carlson CJFarrell MJGrange ZHan BAMollentze NPhelan ALRasmussen ALAlbery GFBett BBrett-Major DMCohen LEDallas TEskew EAFagre ACForbes KMGibb RHalabi SHammer CCKatz RKindrachuk JMuylaert RLNutter FBOgola JOlival KJRourke MRyan SJRoss NSeifert SNSironen TStandley CJTaylor KVenter MWebala PW2024-01-182024-07-252021-09-202024-01-182024-07-252021-11-08Carlson CJ, Farrell MJ, Grange Z, Han BA, Mollentze N, Phelan AL, Rasmussen AL, Albery GF, Bett B, Brett-Major DM, Cohen LE, Dallas T, Eskew EA, Fagre AC, Forbes KM, Gibb R, Halabi S, Hammer CC, Katz R, Kindrachuk J, Muylaert RL, Nutter FB, Ogola J, Olival KJ, Rourke M, Ryan SJ, Ross N, Seifert SN, Sironen T, Standley CJ, Taylor K, Venter M, Webala PW. (2021). The future of zoonotic risk prediction.. Philos Trans R Soc Lond B Biol Sci. 376. 1837. (pp. 20200358-).0962-8436https://mro.massey.ac.nz/handle/10179/70403In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges?(c) 2021 The Author/sCC BY 4.0https://creativecommons.org/licenses/by/4.0/access and benefit sharingepidemic riskglobal healthmachine learningviral ecologyzoonotic riskAnimalsAnimals, WildCOVID-19Disease ReservoirsEcologyGlobal HealthHumansLaboratoriesMachine LearningPandemicsRisk FactorsSARS-CoV-2VirusesZoonosesJournal article10.1098/rstb.2020.03581471-2970journal-article20200358-https://www.ncbi.nlm.nih.gov/pubmed/345381402020.0358