Bayesian space-time SIR modeling of Covid-19 in two US states during the 2020–2021 pandemic

This paper describes the Bayesian SIR modeling of the 3 waves of Covid-19 in two contrasting US states during 2020–2021. A variety of models are evaluated at the county level for goodness-of-fit and an assessment of confounding predictors is also made. It is found that models with three deprivation...

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Detalles Bibliográficos
Autores principales: Lawson, Andrew B., Kim, Joanne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778953/
https://www.ncbi.nlm.nih.gov/pubmed/36548256
http://dx.doi.org/10.1371/journal.pone.0278515
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author Lawson, Andrew B.
Kim, Joanne
author_facet Lawson, Andrew B.
Kim, Joanne
author_sort Lawson, Andrew B.
collection PubMed
description This paper describes the Bayesian SIR modeling of the 3 waves of Covid-19 in two contrasting US states during 2020–2021. A variety of models are evaluated at the county level for goodness-of-fit and an assessment of confounding predictors is also made. It is found that models with three deprivation predictors and neighborhood effects are important. In addition, the work index from Google mobility was also found to provide an increased explanation of the transmission dynamics.
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spelling pubmed-97789532022-12-23 Bayesian space-time SIR modeling of Covid-19 in two US states during the 2020–2021 pandemic Lawson, Andrew B. Kim, Joanne PLoS One Research Article This paper describes the Bayesian SIR modeling of the 3 waves of Covid-19 in two contrasting US states during 2020–2021. A variety of models are evaluated at the county level for goodness-of-fit and an assessment of confounding predictors is also made. It is found that models with three deprivation predictors and neighborhood effects are important. In addition, the work index from Google mobility was also found to provide an increased explanation of the transmission dynamics. Public Library of Science 2022-12-22 /pmc/articles/PMC9778953/ /pubmed/36548256 http://dx.doi.org/10.1371/journal.pone.0278515 Text en © 2022 Lawson, Kim https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Lawson, Andrew B.
Kim, Joanne
Bayesian space-time SIR modeling of Covid-19 in two US states during the 2020–2021 pandemic
title Bayesian space-time SIR modeling of Covid-19 in two US states during the 2020–2021 pandemic
title_full Bayesian space-time SIR modeling of Covid-19 in two US states during the 2020–2021 pandemic
title_fullStr Bayesian space-time SIR modeling of Covid-19 in two US states during the 2020–2021 pandemic
title_full_unstemmed Bayesian space-time SIR modeling of Covid-19 in two US states during the 2020–2021 pandemic
title_short Bayesian space-time SIR modeling of Covid-19 in two US states during the 2020–2021 pandemic
title_sort bayesian space-time sir modeling of covid-19 in two us states during the 2020–2021 pandemic
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778953/
https://www.ncbi.nlm.nih.gov/pubmed/36548256
http://dx.doi.org/10.1371/journal.pone.0278515
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