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A joint hierarchical model for the number of cases and deaths due to COVID-19 across the boroughs of Montreal

As of July 2021, Montreal is the epicentre of the COVID-19 pandemic in Canada with highest number of deaths. We aim to investigate the spatial distribution of the number of cases and deaths due to COVID-19 across the boroughs of Montreal. To this end, we propose that the cumulative numbers of cases...

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Autores principales: Michal, Victoire, Vanciu, Leo, Schmidt, Alexandra M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9126618/
https://www.ncbi.nlm.nih.gov/pubmed/35934331
http://dx.doi.org/10.1016/j.sste.2022.100518
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author Michal, Victoire
Vanciu, Leo
Schmidt, Alexandra M.
author_facet Michal, Victoire
Vanciu, Leo
Schmidt, Alexandra M.
author_sort Michal, Victoire
collection PubMed
description As of July 2021, Montreal is the epicentre of the COVID-19 pandemic in Canada with highest number of deaths. We aim to investigate the spatial distribution of the number of cases and deaths due to COVID-19 across the boroughs of Montreal. To this end, we propose that the cumulative numbers of cases and deaths in the 33 boroughs of Montreal are modelled through a bivariate hierarchical Bayesian model using Poisson distributions. The Poisson means are decomposed in the log scale as the sums of fixed effects and latent effects. The areal median age, the educational level, and the number of beds in long-term care homes are included in the fixed effects. To explore the correlation between cases and deaths inside and across areas, three different bivariate models are considered for the latent effects, namely an independent one, a conditional autoregressive model, and one that allows for both spatially structured and unstructured sources of variability. As the inclusion of spatial effects change some of the fixed effects, we extend the Spatial+ approach to a Bayesian areal set up to investigate the presence of spatial confounding. We find that the model which includes independent latent effects across boroughs performs the best among the ones considered, there appears to be spatial confounding with the diploma and median age variables, and the correlation between the cases and deaths across and within boroughs is always negative.
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spelling pubmed-91266182022-05-24 A joint hierarchical model for the number of cases and deaths due to COVID-19 across the boroughs of Montreal Michal, Victoire Vanciu, Leo Schmidt, Alexandra M. Spat Spatiotemporal Epidemiol Case Study As of July 2021, Montreal is the epicentre of the COVID-19 pandemic in Canada with highest number of deaths. We aim to investigate the spatial distribution of the number of cases and deaths due to COVID-19 across the boroughs of Montreal. To this end, we propose that the cumulative numbers of cases and deaths in the 33 boroughs of Montreal are modelled through a bivariate hierarchical Bayesian model using Poisson distributions. The Poisson means are decomposed in the log scale as the sums of fixed effects and latent effects. The areal median age, the educational level, and the number of beds in long-term care homes are included in the fixed effects. To explore the correlation between cases and deaths inside and across areas, three different bivariate models are considered for the latent effects, namely an independent one, a conditional autoregressive model, and one that allows for both spatially structured and unstructured sources of variability. As the inclusion of spatial effects change some of the fixed effects, we extend the Spatial+ approach to a Bayesian areal set up to investigate the presence of spatial confounding. We find that the model which includes independent latent effects across boroughs performs the best among the ones considered, there appears to be spatial confounding with the diploma and median age variables, and the correlation between the cases and deaths across and within boroughs is always negative. Elsevier Ltd. 2022-08 2022-05-23 /pmc/articles/PMC9126618/ /pubmed/35934331 http://dx.doi.org/10.1016/j.sste.2022.100518 Text en © 2022 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Case Study
Michal, Victoire
Vanciu, Leo
Schmidt, Alexandra M.
A joint hierarchical model for the number of cases and deaths due to COVID-19 across the boroughs of Montreal
title A joint hierarchical model for the number of cases and deaths due to COVID-19 across the boroughs of Montreal
title_full A joint hierarchical model for the number of cases and deaths due to COVID-19 across the boroughs of Montreal
title_fullStr A joint hierarchical model for the number of cases and deaths due to COVID-19 across the boroughs of Montreal
title_full_unstemmed A joint hierarchical model for the number of cases and deaths due to COVID-19 across the boroughs of Montreal
title_short A joint hierarchical model for the number of cases and deaths due to COVID-19 across the boroughs of Montreal
title_sort joint hierarchical model for the number of cases and deaths due to covid-19 across the boroughs of montreal
topic Case Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9126618/
https://www.ncbi.nlm.nih.gov/pubmed/35934331
http://dx.doi.org/10.1016/j.sste.2022.100518
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