Predictive performance of international COVID-19 mortality forecasting models

Forecasts of COVID-19 mortality have been critical inputs into a range of policies, and decision-makers need information about their predictive performance. Here, the authors gather a panel of global epidemiological models and assess their predictive performance across time and space.

Bibliographic Details
Main Authors:	Joseph Friedman, Patrick Liu, Christopher E. Troeger, Austin Carter, Robert C. Reiner, Ryan M. Barber, James Collins, Stephen S. Lim, David M. Pigott, Theo Vos, Simon I. Hay, Christopher J. L. Murray, Emmanuela Gakidou
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-05-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-021-22457-w

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spelling	doaj-52bc9ab11980476da45be7562b888e3f2021-05-11T14:49:10ZengNature Publishing GroupNature Communications2041-17232021-05-0112111310.1038/s41467-021-22457-wPredictive performance of international COVID-19 mortality forecasting modelsJoseph Friedman0Patrick Liu1Christopher E. Troeger2Austin Carter3Robert C. Reiner4Ryan M. Barber5James Collins6Stephen S. Lim7David M. Pigott8Theo Vos9Simon I. Hay10Christopher J. L. Murray11Emmanuela Gakidou12Medical Informatics Home Area, University of California Los AngelesDavid Geffen School of Medicine, University of California Los AngelesInstitute for Health Metrics and Evaluation, University of WashingtonInstitute for Health Metrics and Evaluation, University of WashingtonInstitute for Health Metrics and Evaluation, University of WashingtonInstitute for Health Metrics and Evaluation, University of WashingtonInstitute for Health Metrics and Evaluation, University of WashingtonInstitute for Health Metrics and Evaluation, University of WashingtonInstitute for Health Metrics and Evaluation, University of WashingtonInstitute for Health Metrics and Evaluation, University of WashingtonInstitute for Health Metrics and Evaluation, University of WashingtonInstitute for Health Metrics and Evaluation, University of WashingtonInstitute for Health Metrics and Evaluation, University of WashingtonForecasts of COVID-19 mortality have been critical inputs into a range of policies, and decision-makers need information about their predictive performance. Here, the authors gather a panel of global epidemiological models and assess their predictive performance across time and space.https://doi.org/10.1038/s41467-021-22457-w
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Joseph Friedman Patrick Liu Christopher E. Troeger Austin Carter Robert C. Reiner Ryan M. Barber James Collins Stephen S. Lim David M. Pigott Theo Vos Simon I. Hay Christopher J. L. Murray Emmanuela Gakidou
spellingShingle	Joseph Friedman Patrick Liu Christopher E. Troeger Austin Carter Robert C. Reiner Ryan M. Barber James Collins Stephen S. Lim David M. Pigott Theo Vos Simon I. Hay Christopher J. L. Murray Emmanuela Gakidou Predictive performance of international COVID-19 mortality forecasting models Nature Communications
author_facet	Joseph Friedman Patrick Liu Christopher E. Troeger Austin Carter Robert C. Reiner Ryan M. Barber James Collins Stephen S. Lim David M. Pigott Theo Vos Simon I. Hay Christopher J. L. Murray Emmanuela Gakidou
author_sort	Joseph Friedman
title	Predictive performance of international COVID-19 mortality forecasting models
title_short	Predictive performance of international COVID-19 mortality forecasting models
title_full	Predictive performance of international COVID-19 mortality forecasting models
title_fullStr	Predictive performance of international COVID-19 mortality forecasting models
title_full_unstemmed	Predictive performance of international COVID-19 mortality forecasting models
title_sort	predictive performance of international covid-19 mortality forecasting models
publisher	Nature Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2021-05-01
description	Forecasts of COVID-19 mortality have been critical inputs into a range of policies, and decision-makers need information about their predictive performance. Here, the authors gather a panel of global epidemiological models and assess their predictive performance across time and space.
url	https://doi.org/10.1038/s41467-021-22457-w
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Predictive performance of international COVID-19 mortality forecasting models

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