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Zoonotic risk technology enters the viral emergence toolkit

Show simple item record Colin J. Carlson, Maxwell J. Farrell , Zoe Grange , Barbara A. Han , Nardus Mollentze, Alexandra L. Phelan, Angela L. Rasmussen, Gregory F. Albery , Bernard Bett, David M. BrettMajor, Lily E. Cohen, Tad Dallas, Evan A. Eskew, Anna C. Fagre, Kristian M. Forbes, Rory Gibb, Sam Halabi , Charlotte C. Hammer, Rebecca Katz1 , Jason Kindrachuk, Renata L. Muylaert, Felicia B. Nutter, Joseph Ogola, Kevin J. Olival, Michelle Rourke, Sadie J. Ryan Noam Ross, Stephanie N. Seifert, Tarja Sironen, Claire J. Standley, Kishana Taylor, Marietjie Venter, Paul W. Webala 2021-04-21T12:45:06Z 2021-04-21T12:45:06Z 2021
dc.description.abstract Abstract In light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programs will identify hundreds of novel viruses that might someday pose a threat to humans. Our capacity to identify which viruses are capable of zoonotic emergence depends on the existence of a technology—a machine learning model or other informatic system—that leverages available data on 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? en_US
dc.language.iso en en_US
dc.title Zoonotic risk technology enters the viral emergence toolkit en_US
dc.type Learning Object en_US

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