The future of zoonotic risk prediction.

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Grange, Zoe 
Mollentze, Nardus 

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'.

access and benefit sharing, epidemic risk, global health, machine learning, viral ecology, zoonotic risk, Animals, Animals, Wild, COVID-19, Disease Reservoirs, Ecology, Global Health, Humans, Laboratories, Machine Learning, Pandemics, Risk Factors, SARS-CoV-2, Viruses, Zoonoses
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Philos Trans R Soc Lond B Biol Sci
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The Royal Society
NIAID NIH HHS (U01 AI151797)
Directorate for Biological Sciences (BII 2021909)
University of Toronto (EEB Fellowship)
Wellcome Trust (217221/Z/19/Z)