Developing a COVID-19 mortality risk prediction model when individual-level data are not available

Identification of individuals at risk of severe COVID-19 disease could inform treatment and public health planning. Here, the authors develop and validate a risk prediction model for COVID-19 mortality in Israel by building a model for severe respiratory infection and recalibrating it using COVID-19...

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Autores principales:	Noam Barda, Dan Riesel, Amichay Akriv, Joseph Levy, Uriah Finkel, Gal Yona, Daniel Greenfeld, Shimon Sheiba, Jonathan Somer, Eitan Bachmat, Guy N. Rothblum, Uri Shalit, Doron Netzer, Ran Balicer, Noa Dagan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/b6f41c67d1bb44c58e4540ffa24f6729
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spelling	oai:doaj.org-article:b6f41c67d1bb44c58e4540ffa24f67292021-12-02T19:12:27ZDeveloping a COVID-19 mortality risk prediction model when individual-level data are not available10.1038/s41467-020-18297-92041-1723https://doaj.org/article/b6f41c67d1bb44c58e4540ffa24f67292020-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-18297-9https://doaj.org/toc/2041-1723Identification of individuals at risk of severe COVID-19 disease could inform treatment and public health planning. Here, the authors develop and validate a risk prediction model for COVID-19 mortality in Israel by building a model for severe respiratory infection and recalibrating it using COVID-19 case fatality rates.Noam BardaDan RieselAmichay AkrivJoseph LevyUriah FinkelGal YonaDaniel GreenfeldShimon SheibaJonathan SomerEitan BachmatGuy N. RothblumUri ShalitDoron NetzerRan BalicerNoa DaganNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Noam Barda Dan Riesel Amichay Akriv Joseph Levy Uriah Finkel Gal Yona Daniel Greenfeld Shimon Sheiba Jonathan Somer Eitan Bachmat Guy N. Rothblum Uri Shalit Doron Netzer Ran Balicer Noa Dagan Developing a COVID-19 mortality risk prediction model when individual-level data are not available
description	Identification of individuals at risk of severe COVID-19 disease could inform treatment and public health planning. Here, the authors develop and validate a risk prediction model for COVID-19 mortality in Israel by building a model for severe respiratory infection and recalibrating it using COVID-19 case fatality rates.
format	article
author	Noam Barda Dan Riesel Amichay Akriv Joseph Levy Uriah Finkel Gal Yona Daniel Greenfeld Shimon Sheiba Jonathan Somer Eitan Bachmat Guy N. Rothblum Uri Shalit Doron Netzer Ran Balicer Noa Dagan
author_facet	Noam Barda Dan Riesel Amichay Akriv Joseph Levy Uriah Finkel Gal Yona Daniel Greenfeld Shimon Sheiba Jonathan Somer Eitan Bachmat Guy N. Rothblum Uri Shalit Doron Netzer Ran Balicer Noa Dagan
author_sort	Noam Barda
title	Developing a COVID-19 mortality risk prediction model when individual-level data are not available
title_short	Developing a COVID-19 mortality risk prediction model when individual-level data are not available
title_full	Developing a COVID-19 mortality risk prediction model when individual-level data are not available
title_fullStr	Developing a COVID-19 mortality risk prediction model when individual-level data are not available
title_full_unstemmed	Developing a COVID-19 mortality risk prediction model when individual-level data are not available
title_sort	developing a covid-19 mortality risk prediction model when individual-level data are not available
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/b6f41c67d1bb44c58e4540ffa24f6729
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Developing a COVID-19 mortality risk prediction model when individual-level data are not available

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