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The Future of Zoonotic Risk Prediction

Philosophical Transactions of the Royal Society B

Image highlights the steps to predicting and preventing zoonotic risks and highlights barriers to success.
Figure 1 from The Future of Zoonotic Risk Prediction. Zoonotic risk technology can be part of a broader scientific pipeline that connects viral discovery and wildlife disease surveillance to the development of biomedical and ecological solutions to predict, prevent and prepare for future outbreaks.

RYANThe Future of Zoonotic Risk Prediction

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. Brett-Major, Lily E. Cohen, Tad Dallas, Evan A. Eskew, Anna C. Fagre, Kristian M. Forbes, Rory Gibb, Sam Halabi, Charlotte C. Hammer, Rebecca Katz, 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, and Paul W. Wobble

Article first published online: 20 SEP 2021 in the Philosophical Transactions of the Royal Society B

DOI: 10.1098/rstb.2020.0358

The Future of Zoonotic Risk Prediction is an opinion piece written by 33 researchers, describing the current toolbox of strategies in zoonotic disease emergence risk prediction, and how the challenges of data availability and sharing, particularly in the international setting, comprise the framework for the future. Dr Sadie J. Ryan is a co-author of this piece, which arose from a workshop on the zoonotic toolbox, as part of the Viral Emergence Research Initiative (viralemergence.org).

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?

Read the full publication at Philosophical Transactions of the Royal Society B