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

Nazia, Nushrat; Law, Jane; Butt, Zahid Ahmad

Nazia, Nushrat; Law, Jane; Butt, Zahid Ahmad.

Afiliação

Nazia N; School of Public Health Sciences, University of Waterloo, 200 University Ave., Waterloo, ON, N2L3G1, Canada. nnazia@uwaterloo.ca.
Law J; School of Public Health Sciences, University of Waterloo, 200 University Ave., Waterloo, ON, N2L3G1, Canada.
Butt ZA; School of Planning, University of Waterloo, 200 University Ave., Waterloo, ON, N2L3G1, Canada.

Sci Rep ; 12(1): 9369, 2022 06 07.

Article em En | MEDLINE | ID: mdl-35672355

Assuntos

COVID-19; Teorema de Bayes; COVID-19/epidemiologia; Humanos; Incidência; Pandemias; Tecnologia de Sensoriamento Remoto; Análise Espaço-Temporal

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: COVID-19 Tipo de estudo: Incidence_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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