Authors: Carlson, Colin J, Farrell, Maxwell J, Grange, Zoe, Han, Barbara A, Mollentze, Nardus, Phelan, Alexandra L, Rasmussen, Angela L, Albery, Gregory F, Bett, Bernard, Brett-Major, David M, Cohen, Lily E, Dallas, Tad, Eskew, Evan A, Fagre, Anna C, Forbes, Kristian M, Gibb, Rory, Halabi, Sam, Hammer, Charlotte C, Katz, Rebecca, Kindrachuk, Jason, Muylaert, Renata L, Nutter, Felicia B, Ogola, Joseph, Olival, Kevin J, Rourke, Michelle, Ryan, Sadie J, Ross, Noam, Seifert, Stephanie N, Sironen, Tarja, Standley, Claire J, Taylor, Kishana, Venter, Marietjie, Webala, Paul W
Venue: Philosophical Transactions of the Royal Society B: Biological Sciences
Type: Publication
Abstract: 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 wor...
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Topics: 
Data science
Environmental planning
Risk analysis (engineering)
DOI:
10.1098/rstb.2020.0358
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