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Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling
Research article (Scientific Reports, 2022) · cited 18× · AI/ML
Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling
Summary
Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling is a scholarly article[1].
Key Facts
Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling's instance of is recorded as scholarly article[2].
References
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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling. Retrieved May 24, 2026, from https://4ort.xyz/entity/identifying-spatiotemporal-patterns-of-covid-19-transmissions-and-the-drivers-of-the-patterns-in-toronto-a-bayesian-hier
MLA“Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/identifying-spatiotemporal-patterns-of-covid-19-transmissions-and-the-drivers-of-the-patterns-in-toronto-a-bayesian-hier.
BibTeX@misc{4ortxyz_identifying-spatiotemporal-patterns-of-covid-19-transmissions-and-the-drivers-of-the-patterns-in-toronto-a-bayesian-hier_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling}}, year = {2026}, url = {https://4ort.xyz/entity/identifying-spatiotemporal-patterns-of-covid-19-transmissions-and-the-drivers-of-the-patterns-in-toronto-a-bayesian-hier}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling — https://4ort.xyz/entity/identifying-spatiotemporal-patterns-of-covid-19-transmissions-and-the-drivers-of-the-patterns-in-toronto-a-bayesian-hier (retrieved 2026-05-24)
