Using spatial and temporal modeling to visualize the effects of U.S. state issued stay at home orders on COVID-19

Abstract Coronavirus disease 2019 dominated and augmented many aspects of life beginning in early 2020. Related research and data generation developed alongside its spread. We developed a Bayesian spatio-temporal Poisson disease mapping model for estimating real-time characteristics of the coronavir...

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Autores principales:	Rachel Carroll, Christopher R. Prentice
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/845ae2b8e32c447e850ef6fca873ad41
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spelling	oai:doaj.org-article:845ae2b8e32c447e850ef6fca873ad412021-12-02T16:14:56ZUsing spatial and temporal modeling to visualize the effects of U.S. state issued stay at home orders on COVID-1910.1038/s41598-021-93433-z2045-2322https://doaj.org/article/845ae2b8e32c447e850ef6fca873ad412021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93433-zhttps://doaj.org/toc/2045-2322Abstract Coronavirus disease 2019 dominated and augmented many aspects of life beginning in early 2020. Related research and data generation developed alongside its spread. We developed a Bayesian spatio-temporal Poisson disease mapping model for estimating real-time characteristics of the coronavirus disease in the United States. We also created several dashboards for visualization of the statistical model for fellow researchers and simpler spatial and temporal representations of the disease for consumption by analysts and data scientists in the policymaking community in our region. Findings suggest that the risk of confirmed cases is higher for health regions under partial stay at home orders and lower in health regions under full stay at home orders, when compared to before stay at home orders were declared. These results confirm the benefit of state-issued stay at home orders as well as suggest compliance to the directives towards the older population for adhering to social distancing guidelines.Rachel CarrollChristopher R. PrenticeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rachel Carroll Christopher R. Prentice Using spatial and temporal modeling to visualize the effects of U.S. state issued stay at home orders on COVID-19
description	Abstract Coronavirus disease 2019 dominated and augmented many aspects of life beginning in early 2020. Related research and data generation developed alongside its spread. We developed a Bayesian spatio-temporal Poisson disease mapping model for estimating real-time characteristics of the coronavirus disease in the United States. We also created several dashboards for visualization of the statistical model for fellow researchers and simpler spatial and temporal representations of the disease for consumption by analysts and data scientists in the policymaking community in our region. Findings suggest that the risk of confirmed cases is higher for health regions under partial stay at home orders and lower in health regions under full stay at home orders, when compared to before stay at home orders were declared. These results confirm the benefit of state-issued stay at home orders as well as suggest compliance to the directives towards the older population for adhering to social distancing guidelines.
format	article
author	Rachel Carroll Christopher R. Prentice
author_facet	Rachel Carroll Christopher R. Prentice
author_sort	Rachel Carroll
title	Using spatial and temporal modeling to visualize the effects of U.S. state issued stay at home orders on COVID-19
title_short	Using spatial and temporal modeling to visualize the effects of U.S. state issued stay at home orders on COVID-19
title_full	Using spatial and temporal modeling to visualize the effects of U.S. state issued stay at home orders on COVID-19
title_fullStr	Using spatial and temporal modeling to visualize the effects of U.S. state issued stay at home orders on COVID-19
title_full_unstemmed	Using spatial and temporal modeling to visualize the effects of U.S. state issued stay at home orders on COVID-19
title_sort	using spatial and temporal modeling to visualize the effects of u.s. state issued stay at home orders on covid-19
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/845ae2b8e32c447e850ef6fca873ad41
work_keys_str_mv	AT rachelcarroll usingspatialandtemporalmodelingtovisualizetheeffectsofusstateissuedstayathomeordersoncovid19 AT christopherrprentice usingspatialandtemporalmodelingtovisualizetheeffectsofusstateissuedstayathomeordersoncovid19
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Using spatial and temporal modeling to visualize the effects of U.S. state issued stay at home orders on COVID-19

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