Using drivers and transmission pathways to identify SARS-like coronavirus spillover risk hotspots.

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dc.citation.issue1
dc.citation.volume14
dc.contributor.authorMuylaert RL
dc.contributor.authorWilkinson DA
dc.contributor.authorKingston T
dc.contributor.authorD'Odorico P
dc.contributor.authorRulli MC
dc.contributor.authorGalli N
dc.contributor.authorJohn RS
dc.contributor.authorAlviola P
dc.contributor.authorHayman DTS
dc.coverage.spatialEngland
dc.date.accessioned2024-09-10T01:31:13Z
dc.date.available2024-09-10T01:31:13Z
dc.date.issued2023-10-27
dc.description.abstractThe emergence of SARS-like coronaviruses is a multi-stage process from wildlife reservoirs to people. Here we characterize multiple drivers-landscape change, host distribution, and human exposure-associated with the risk of spillover of zoonotic SARS-like coronaviruses to help inform surveillance and mitigation activities. We consider direct and indirect transmission pathways by modeling four scenarios with livestock and mammalian wildlife as potential and known reservoirs before examining how access to healthcare varies within clusters and scenarios. We found 19 clusters with differing risk factor contributions within a single country (N = 9) or transboundary (N = 10). High-risk areas were mainly closer (11-20%) rather than far ( < 1%) from healthcare. Areas far from healthcare reveal healthcare access inequalities, especially Scenario 3, which includes wild mammals and not livestock as secondary hosts. China (N = 2) and Indonesia (N = 1) had clusters with the highest risk. Our findings can help stakeholders in land use planning, integrating healthcare implementation and One Health actions.
dc.description.confidentialfalse
dc.edition.edition2023
dc.format.pagination6854-
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/37891177
dc.identifier.citationMuylaert RL, Wilkinson DA, Kingston T, D'Odorico P, Rulli MC, Galli N, John RS, Alviola P, Hayman DTS. (2023). Using drivers and transmission pathways to identify SARS-like coronavirus spillover risk hotspots.. Nat Commun. 14. 1. (pp. 6854-).
dc.identifier.doi10.1038/s41467-023-42627-2
dc.identifier.eissn2041-1723
dc.identifier.elements-typejournal-article
dc.identifier.issn2041-1723
dc.identifier.number6854
dc.identifier.pii10.1038/s41467-023-42627-2
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/71441
dc.languageeng
dc.publisherSpringer Nature Limited
dc.publisher.urihttps://www.biorxiv.org/content/10.1101/2022.12.08.518776v2
dc.relation.isPartOfNat Commun
dc.rights(c) 2023 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectAnimals
dc.subjectHumans
dc.subjectSevere acute respiratory syndrome-related coronavirus
dc.subjectAnimals, Wild
dc.subjectMammals
dc.subjectRisk Factors
dc.subjectLivestock
dc.titleUsing drivers and transmission pathways to identify SARS-like coronavirus spillover risk hotspots.
dc.typeJournal article
pubs.elements-id484032
pubs.organisational-groupOther
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