article · Philosophical Transactions of the Royal Society B Biological Sciences
Increased global investment in wildlife virology following the COVID-19 pandemic is expected to yield hundreds of newly identified animal viruses. Evaluating the potential threat of these pathogens through conventional laboratory characterisation is an extensive task, prompting interest in data-driven rubrics and machine learning models trained on known zoonoses. Drawing on findings from an interdisciplinary workshop, this synthesis examines the prerequisites for developing and applying zoonotic risk technologies, highlighting the necessity of open data, equity, and collaborative research across disciplines. It also investigates the wider implications of such tools for global health and pandemic prevention, addressing crucial questions about who will control, access, and benefit from the technology, as well as the new operational and ethical challenges its implementation could create.
Preventing future pandemics requires understanding which newly discovered animal viruses threaten human populations. Machine learning models could help prioritise these pathogens for laboratory testing, but successful deployment depends on fair data sharing, global cooperation, and clear governance. Evaluating who controls and benefits from these predictive technologies is vital for ensuring they effectively safeguard global public health.
The abstract outlines early-stage conceptual synthesis regarding machine learning models and data-driven rubrics for predicting zoonotic pathogen risks. Potential users include global health organisations, virologists, and surveillance programmes seeking to prioritise animal viruses for laboratory testing. However, the work focuses on foundational governance, equity, and data prerequisites rather than commercial deployment, indicating that practical applications remain at an early exploratory stage.
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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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DOI: 10.1098/rstb.2020.0358
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