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dc.contributor.author | Carlson, Colin J.![]() |
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dc.contributor.author | Farrell, Maxwell J.![]() |
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dc.contributor.author | Grange, Zoe![]() |
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dc.contributor.author | Han, Barbara A.![]() |
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dc.contributor.author | Mollentze, Nardus![]() |
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dc.contributor.author | Phelan, Alexandra L.![]() |
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dc.contributor.author | Rasmussen, Angela L.![]() |
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dc.contributor.author | Albery, Gregory F.![]() |
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dc.contributor.author | Bett, Bernard![]() |
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dc.contributor.author | Brett-Major, David M.![]() |
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dc.contributor.author | Cohen, Lily E.![]() |
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dc.contributor.author | Dallas, Tad![]() |
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dc.contributor.author | Eskew, Evan A.![]() |
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dc.contributor.author | Fagre, Anna C.![]() |
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dc.contributor.author | Forbes, Kristian M.![]() |
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dc.contributor.author | Gibb, Rory![]() |
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dc.contributor.author | Halabi, Sam![]() |
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dc.contributor.author | Hammer, Charlotte C.![]() |
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dc.contributor.author | Katz, Rebecca![]() |
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dc.contributor.author | Kindrachuk, Jason![]() |
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dc.contributor.author | Muylaert, Renata L.![]() |
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dc.contributor.author | Nutter, Felicia B.![]() |
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dc.contributor.author | Ogola, Joseph![]() |
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dc.contributor.author | Olival, Kevin J.![]() |
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dc.contributor.author | Rourke, Michelle![]() |
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dc.contributor.author | Ryan, Sadie J.![]() |
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dc.contributor.author | Ross, Noam![]() |
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dc.contributor.author | Seifert, Stephanie N.![]() |
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dc.contributor.author | Sironen, Tarja![]() |
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dc.contributor.author | Standley, Claire J.![]() |
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dc.contributor.author | Taylor, Kishana![]() |
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dc.contributor.author | Venter, Marietjie![]() |
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dc.contributor.author | Webala, Paul W.![]() |
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dc.date.accessioned | 2022-10-05T13:03:12Z | |
dc.date.available | 2022-10-05T13:03:12Z | |
dc.date.issued | 2021 | |
dc.description.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? This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’. | en_US |
dc.description.department | Medical Virology | en_US |
dc.description.librarian | am2022 | en_US |
dc.description.sponsorship | NSF BII 2021909; the University of Toronto EEB Fellowship; the Wellcome Trust; the National Institute of Allergy and Infectious Diseases of the National Institutes of Health and the Defense Threat Reduction Agency. | en_US |
dc.description.uri | http://rstb.royalsocietypublishing.org | en_US |
dc.identifier.citation | Carlson, C.J., Farrell, M.J., Grange, Z. et al. 2021 The future of zoonotic risk prediction. Philosophical Transactions of the Royal Society B-Biological Sciences 376: 20200358. https://DOI.org/10.1098/rstb.2020.0358. | en_US |
dc.identifier.issn | 0962-8436 (print) | |
dc.identifier.issn | 1471-2970 (online) | |
dc.identifier.other | 10.1098/rstb.2020.0358 | |
dc.identifier.uri | https://repository.up.ac.za/handle/2263/87526 | |
dc.language.iso | en | en_US |
dc.publisher | Royal Society | en_US |
dc.rights | © 2021 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License. | en_US |
dc.subject | Zoonotic risk | en_US |
dc.subject | Epidemic risk | en_US |
dc.subject | Access and benefit sharing | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Global health | en_US |
dc.subject | Viral ecology | en_US |
dc.title | The future of zoonotic risk prediction | en_US |
dc.type | Article | en_US |