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Zoonotic risk technology enters the viral emergence toolkit
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
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?
