Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions

Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions

Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19. In this study, the authors develop a spatiotemporal machine learning model to predict county level new cases in the US using a variety of predic...

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Autores principales: Behzad Vahedi, Morteza Karimzadeh, Hamidreza Zoraghein
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/2daf6579f3da4705995a961cf61f12cf
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spelling oai:doaj.org-article:2daf6579f3da4705995a961cf61f12cf2021-11-14T12:36:07ZSpatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions10.1038/s41467-021-26742-62041-1723https://doaj.org/article/2daf6579f3da4705995a961cf61f12cf2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-26742-6https://doaj.org/toc/2041-1723Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19. In this study, the authors develop a spatiotemporal machine learning model to predict county level new cases in the US using a variety of predictive features.Behzad VahediMorteza KarimzadehHamidreza ZoragheinNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-15 (2021)
institution DOAJ
collection DOAJ
language EN
topic Science
Q
spellingShingle Science
Q
Behzad Vahedi
Morteza Karimzadeh
Hamidreza Zoraghein
Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions
description Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19. In this study, the authors develop a spatiotemporal machine learning model to predict county level new cases in the US using a variety of predictive features.
format article
author Behzad Vahedi
Morteza Karimzadeh
Hamidreza Zoraghein
author_facet Behzad Vahedi
Morteza Karimzadeh
Hamidreza Zoraghein
author_sort Behzad Vahedi
title Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions
title_short Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions
title_full Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions
title_fullStr Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions
title_full_unstemmed Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions
title_sort spatiotemporal prediction of covid-19 cases using inter- and intra-county proxies of human interactions
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/2daf6579f3da4705995a961cf61f12cf
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AT mortezakarimzadeh spatiotemporalpredictionofcovid19casesusinginterandintracountyproxiesofhumaninteractions
AT hamidrezazoraghein spatiotemporalpredictionofcovid19casesusinginterandintracountyproxiesofhumaninteractions
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