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The future of zoonotic risk prediction.

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cam.issuedOnline2021-09-20
dc.contributor.authorCarlson, Colin J
dc.contributor.authorFarrell, Maxwell J
dc.contributor.authorGrange, Zoe
dc.contributor.authorHan, Barbara A
dc.contributor.authorMollentze, Nardus
dc.contributor.authorPhelan, Alexandra L
dc.contributor.authorRasmussen, Angela L
dc.contributor.authorAlbery, Gregory F
dc.contributor.authorBett, Bernard
dc.contributor.authorBrett-Major, David M
dc.contributor.authorCohen, Lily E
dc.contributor.authorDallas, Tad
dc.contributor.authorEskew, Evan A
dc.contributor.authorFagre, Anna C
dc.contributor.authorForbes, Kristian M
dc.contributor.authorGibb, Rory
dc.contributor.authorHalabi, Sam
dc.contributor.authorHammer, Charlotte C
dc.contributor.authorKatz, Rebecca
dc.contributor.authorKindrachuk, Jason
dc.contributor.authorMuylaert, Renata L
dc.contributor.authorNutter, Felicia B
dc.contributor.authorOgola, Joseph
dc.contributor.authorOlival, Kevin J
dc.contributor.authorRourke, Michelle
dc.contributor.authorRyan, Sadie J
dc.contributor.authorRoss, Noam
dc.contributor.authorSeifert, Stephanie N
dc.contributor.authorSironen, Tarja
dc.contributor.authorStandley, Claire J
dc.contributor.authorTaylor, Kishana
dc.contributor.authorVenter, Marietjie
dc.contributor.authorWebala, Paul W
dc.contributor.orcidCarlson, Colin J [0000-0001-6960-8434]
dc.contributor.orcidFarrell, Maxwell J [0000-0003-0452-6993]
dc.contributor.orcidHan, Barbara A [0000-0002-9948-3078]
dc.contributor.orcidBrett-Major, David M [0000-0002-7583-8495]
dc.contributor.orcidDallas, Tad [0000-0003-3328-9958]
dc.contributor.orcidEskew, Evan A [0000-0002-1153-5356]
dc.contributor.orcidFagre, Anna C [0000-0002-0969-5078]
dc.contributor.orcidForbes, Kristian M [0000-0002-2112-2707]
dc.contributor.orcidGibb, Rory [0000-0002-0965-1649]
dc.contributor.orcidHammer, Charlotte C [0000-0002-8288-0288]
dc.contributor.orcidRyan, Sadie J [0000-0002-4308-6321]
dc.date.accessioned2021-10-22T00:38:10Z
dc.date.available2021-10-22T00:38:10Z
dc.date.issued2021-11-08
dc.date.updated2021-10-22T00:38:10Z
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? This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.
dc.identifier.citationPhilosophical transactions of the Royal Society of London. Series B, Biological sciences, volume 376, issue 1837, page 20200358
dc.identifier.doi10.17863/CAM.77196
dc.identifier.eissn1471-2970
dc.identifier.issn0962-8436
dc.identifier.otherPMC8450624
dc.identifier.other34538140
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/329750
dc.languageeng
dc.language.isoeng
dc.publisherThe Royal Society
dc.publisher.urlhttp://dx.doi.org/10.1098/rstb.2020.0358
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceessn: 1471-2970
dc.sourcenlmid: 7503623
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.typeArticle
dcterms.dateAccepted2021-07-15
prism.publicationNamePhilos Trans R Soc Lond B Biol Sci
pubs.funder-project-idNIAID NIH HHS (U01 AI151797)
pubs.funder-project-idDirectorate for Biological Sciences (BII 2021909)
pubs.funder-project-idUniversity of Toronto (EEB Fellowship)
pubs.funder-project-idWellcome Trust (217221/Z/19/Z)
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1098/rstb.2020.0358

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