@article{Carlson_C-2021_43261, title = {The future of zoonotic risk prediction}, author = {Carlson, C. and Farrell, M. and Grange, Z. and Han, B. and Mollentze, N. and Phelan, A. and Rusmassen, A. and Albery, G. and Bett, B. and Brett-Major, D. and Cohen, L. and Dallas, T. and Eskew, E. and Fagre, A. and Forbes, K. and Gibb, R. and Halabi, S. and Hammer, C. and Katz, R. and Kindrachuk, J. and Muylaert, R. and Nutter, F. and Ogola, J. and Olival, K. and Rourke, M. and Ryan, S. and Ross, N. and Seifert, S. and Sironen, T. and Standley, C. and Taylor, K. and Venter, M. and Webala, P.}, month = {sep}, year = {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?}, volume = {376}, issue = {1837}, journal = {Philosophical Transactions of the Royal Society of London Series B: Biological Sciences}, publisher = {The Royal Society}, url = {https://doi.org/10.1098/rstb.2020.0358}, }