The future of zoonotic risk prediction

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dc.citation.issue1837
dc.citation.volume376
dc.contributor.authorCarlson CJ
dc.contributor.authorFarrell MJ
dc.contributor.authorGrange Z
dc.contributor.authorHan BA
dc.contributor.authorMollentze N
dc.contributor.authorPhelan AL
dc.contributor.authorRasmussen AL
dc.contributor.authorAlbery GF
dc.contributor.authorBett B
dc.contributor.authorBrett-Major DM
dc.contributor.authorCohen LE
dc.contributor.authorDallas T
dc.contributor.authorEskew EA
dc.contributor.authorFagre AC
dc.contributor.authorForbes KM
dc.contributor.authorGibb R
dc.contributor.authorHalabi S
dc.contributor.authorHammer CC
dc.contributor.authorKatz R
dc.contributor.authorKindrachuk J
dc.contributor.authorMuylaert RL
dc.contributor.authorNutter FB
dc.contributor.authorOgola J
dc.contributor.authorOlival KJ
dc.contributor.authorRourke M
dc.contributor.authorRyan SJ
dc.contributor.authorRoss N
dc.contributor.authorSeifert SN
dc.contributor.authorSironen T
dc.contributor.authorStandley CJ
dc.contributor.authorTaylor K
dc.contributor.authorVenter M
dc.contributor.authorWebala PW
dc.coverage.spatialEngland
dc.date.accessioned2024-01-18T19:10:21Z
dc.date.accessioned2024-07-25T06:33:12Z
dc.date.available2021-09-20
dc.date.available2024-01-18T19:10:21Z
dc.date.available2024-07-25T06:33:12Z
dc.date.issued2021-11-08
dc.description.abstractIn 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?
dc.description.confidentialfalse
dc.edition.editionNovember 2021
dc.format.pagination20200358-
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/34538140
dc.identifier.citationCarlson CJ, Farrell MJ, Grange Z, Han BA, Mollentze N, Phelan AL, Rasmussen AL, Albery GF, Bett B, Brett-Major DM, Cohen LE, Dallas T, Eskew EA, Fagre AC, Forbes KM, Gibb R, Halabi S, Hammer CC, Katz R, Kindrachuk J, Muylaert RL, Nutter FB, Ogola J, Olival KJ, Rourke M, Ryan SJ, Ross N, Seifert SN, Sironen T, Standley CJ, Taylor K, Venter M, Webala PW. (2021). The future of zoonotic risk prediction.. Philos Trans R Soc Lond B Biol Sci. 376. 1837. (pp. 20200358-).
dc.identifier.doi10.1098/rstb.2020.0358
dc.identifier.eissn1471-2970
dc.identifier.elements-typejournal-article
dc.identifier.issn0962-8436
dc.identifier.number2020.0358
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/70403
dc.languageeng
dc.publisherThe Royal Society
dc.publisher.urihttps://royalsocietypublishing.org/doi/10.1098/rstb.2020.0358#d74950663e1
dc.relation.isPartOfPhilos Trans R Soc Lond B Biol Sci
dc.rights(c) 2021 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectaccess and benefit sharing
dc.subjectepidemic risk
dc.subjectglobal health
dc.subjectmachine learning
dc.subjectviral ecology
dc.subjectzoonotic risk
dc.subjectAnimals
dc.subjectAnimals, Wild
dc.subjectCOVID-19
dc.subjectDisease Reservoirs
dc.subjectEcology
dc.subjectGlobal Health
dc.subjectHumans
dc.subjectLaboratories
dc.subjectMachine Learning
dc.subjectPandemics
dc.subjectRisk Factors
dc.subjectSARS-CoV-2
dc.subjectViruses
dc.subjectZoonoses
dc.titleThe future of zoonotic risk prediction
dc.typeJournal article
pubs.elements-id448759
pubs.organisational-groupOther
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