The future of zoonotic risk prediction
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,...
| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Royal Society
2021
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/115078 |
| _version_ | 1855539370526769152 |
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| author | Carlson, C.J. Farrell, M.J. Grange, Z. Han, B.A. Mollentze, N. Phelan, A.L. Rasmussen, A.L. Albery, G.F. Bett, Bernard K. Brett-Major, D.M. Cohen, L.E. Dallas, T. Eskew, E.A. Fagre, A.C. Forbes, K.M. Gibb, R. Halabi, S. Hammer, C.C. Katz, R. Kindrachuk, J. Muylaert, R.L. Nutter, F.B. Ogola, J. Olival, K.J. Rourke, M. Ryan, S.J. Ross, N. Seifert, S.N. Sironen, T. Standley, C.J. Taylor, K. Venter, M. Webala, P.W. |
| author_browse | Albery, G.F. Bett, Bernard K. Brett-Major, D.M. Carlson, C.J. Cohen, L.E. Dallas, T. Eskew, E.A. Fagre, A.C. Farrell, M.J. Forbes, K.M. Gibb, R. Grange, Z. Halabi, S. Hammer, C.C. Han, B.A. Katz, R. Kindrachuk, J. Mollentze, N. Muylaert, R.L. Nutter, F.B. Ogola, J. Olival, K.J. Phelan, A.L. Rasmussen, A.L. Ross, N. Rourke, M. Ryan, S.J. Seifert, S.N. Sironen, T. Standley, C.J. Taylor, K. Venter, M. Webala, P.W. |
| author_facet | Carlson, C.J. Farrell, M.J. Grange, Z. Han, B.A. Mollentze, N. Phelan, A.L. Rasmussen, A.L. Albery, G.F. Bett, Bernard K. Brett-Major, D.M. Cohen, L.E. Dallas, T. Eskew, E.A. Fagre, A.C. Forbes, K.M. Gibb, R. Halabi, S. Hammer, C.C. Katz, R. Kindrachuk, J. Muylaert, R.L. Nutter, F.B. Ogola, J. Olival, K.J. Rourke, M. Ryan, S.J. Ross, N. Seifert, S.N. Sironen, T. Standley, C.J. Taylor, K. Venter, M. Webala, P.W. |
| author_sort | Carlson, C.J. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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’. |
| format | Journal Article |
| id | CGSpace115078 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Royal Society |
| publisherStr | Royal Society |
| record_format | dspace |
| spelling | CGSpace1150782025-08-15T13:22:54Z The future of zoonotic risk prediction Carlson, C.J. Farrell, M.J. Grange, Z. Han, B.A. Mollentze, N. Phelan, A.L. Rasmussen, A.L. Albery, G.F. Bett, Bernard K. Brett-Major, D.M. Cohen, L.E. Dallas, T. Eskew, E.A. Fagre, A.C. Forbes, K.M. Gibb, R. Halabi, S. Hammer, C.C. Katz, R. Kindrachuk, J. Muylaert, R.L. Nutter, F.B. Ogola, J. Olival, K.J. Rourke, M. Ryan, S.J. Ross, N. Seifert, S.N. Sironen, T. Standley, C.J. Taylor, K. Venter, M. Webala, P.W. zoonoses health 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’. 2021-11-08 2021-09-21T18:12:24Z 2021-09-21T18:12:24Z Journal Article https://hdl.handle.net/10568/115078 en Open Access Royal Society Carlson, C.J., Farrell, M.J., Grange, Z., Han, B.A., Mollentze, N., Phelan, A.L., Rasmussen, A.L., Albery, G.F., Bett, B., Brett-Major, D.M., Cohen, L.E., Dallas, T., Eskew, E.A., Fagre, A.C., Forbes, K.M., Gibb, R., Halabi, S., Hammer, C.C., Katz, R., Kindrachuk, J., Muylaert, R.L., Nutter, F.B., Ogola, J., Olival, K.J., Rourke, M., Ryan, S.J., Ross, N., Seifert, S.N., Sironen, T., Standley, C.J., Taylor, K., Venter, M. and Webala, P.W. 2021. The future of zoonotic risk prediction. Philosophical Transactions of the Royal Society B: Biological Sciences 376(1837): 20200358. |
| spellingShingle | zoonoses health Carlson, C.J. Farrell, M.J. Grange, Z. Han, B.A. Mollentze, N. Phelan, A.L. Rasmussen, A.L. Albery, G.F. Bett, Bernard K. Brett-Major, D.M. Cohen, L.E. Dallas, T. Eskew, E.A. Fagre, A.C. Forbes, K.M. Gibb, R. Halabi, S. Hammer, C.C. Katz, R. Kindrachuk, J. Muylaert, R.L. Nutter, F.B. Ogola, J. Olival, K.J. Rourke, M. Ryan, S.J. Ross, N. Seifert, S.N. Sironen, T. Standley, C.J. Taylor, K. Venter, M. Webala, P.W. The future of zoonotic risk prediction |
| title | The future of zoonotic risk prediction |
| title_full | The future of zoonotic risk prediction |
| title_fullStr | The future of zoonotic risk prediction |
| title_full_unstemmed | The future of zoonotic risk prediction |
| title_short | The future of zoonotic risk prediction |
| title_sort | future of zoonotic risk prediction |
| topic | zoonoses health |
| url | https://hdl.handle.net/10568/115078 |
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