Colin J. Carlson, Georgetown University Medical Center
Maxwell J. Farrell, University of Toronto
Zoe Grange, Public Health Scotland
Barbara A. Han, Cary Institute of Ecosystem Studies
Nardus Mollentze, University of Glasgow Centre for Virus Research
Alexandra L. Phelan, Georgetown University Medical Center
Angela L. Rasmussen, Georgetown University Medical Center
Gregory F. Albery, Georgetown University
Bernard Bett, International Livestock Research Institute
David Brett-Major, University of Nebraska Medical CenterFollow
Lily E. Cohen, Icahn School of Medicine at Mount Sinai
Tad Dallas, Louisiana State University
Evan A. Eskew, Pacific Lutheran University
Anna C. Fagre, Colorado State University
Kristian M. Forbes, University of Arkansas
Rory Gibb, London School of Hygiene and Tropical Medicine
Sam Halabi, Georgetown University Law Center
Charlotte C. Hammer, University of Cambridge
Rebecca Katz, Georgetown University Medical Center
Jason Kindrachuk, University of Manitoba
Renata L. Muylaert, Massey University
Felicia B. Nutter, Tufts University
Joseph Ogola, University of Nairobi
Kevin J. Olival, EcoHealth Alliance
Michelle Rourke, Griffith University
Sadie J. Ryan, University of Florida
Noam Ross, EcoHealth Alliance
Stephanie N. Seifert, Washington State University
Tarja Sironen, University of Helsinki
Claire J. Standley, Georgetown University Medical Center
Kishana Taylor, Carnegie Mellon University
Marietjie Venter, University of Pretoria
Paul W. Webala, Maasai Mara University

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Journal Title

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences

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In 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? This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.



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Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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