The future of zoonotic risk prediction
Colin J. Carlson1, 2, Maxwell J. Farrell3 , Zoe Grange4 , Barbara A. Han5 , Nardus Mollentze6,7, Alexandra L. Phelan1,8, Angela L. Rasmussen1 , Gregory F. Albery9 , Bernard Bett10, David M. Brett-Major11, Lily E. Cohen12, Tad Dallas13, Evan A. Eskew14, Anna C. Fagre15, Kristian M. Forbes16, Rory Gibb17,18, Sam Halabi8 , Charlotte C. Hammer19, Rebecca Katz1 , Jason Kindrachuk20, Renata L. Muylaert21, Felicia B. Nutter22,23, Joseph Ogola24, Kevin J. Olival25, Michelle Rourke26, Sadie J. Ryan27,28, Noam Ross25, Stephanie N. Seifert29, Tarja Sironen30,31, Claire J. Standley1,2, Kishana Taylor32, Marietjie Venter33 and Paul W. Webala34
Date:
2021
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
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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