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

Spatiotemporal patterns and trends of COVID-19 at a local spatial scale using Bayesian approaches are hardly observed in literature. Also, studies rarely use satellite-derived long time-series data on the environment to predict COVID-19 risk at a spatial scale. In this study, we modelled the COVID-1...

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Detalles Bibliográficos
Autores principales: Nazia, Nushrat, Law, Jane, Butt, Zahid Ahmad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9172088/
https://www.ncbi.nlm.nih.gov/pubmed/35672355
http://dx.doi.org/10.1038/s41598-022-13403-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9172088/
https://www.ncbi.nlm.nih.gov/pubmed/35672355
http://dx.doi.org/10.1038/s41598-022-13403-x

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