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’.