Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling

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Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 2045-2322

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling ; volume:12 ; number:1 ; day:7 ; month:6 ; year:2022 ; pages:1-13 ; date:12.2022
Scientific reports ; 12, Heft 1 (7.6.2022), 1-13, 12.2022

Creator: Nazia, Nushrat
Law, Jane
Butt, Zahid Ahmad

Contributor: SpringerLink (Online service)

DOI: 10.1038/s41598-022-13403-x

URN: urn:nbn:de:101:1-2022080421354196973404

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:29 AM CEST

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Nazia, Nushrat
Law, Jane
Butt, Zahid Ahmad
SpringerLink (Online service)

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Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling

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