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An expert judgment model to predict early stages of the COVID-19 outbreak in the United States

During early stages of the COVID-19 pandemic, forecasts provided actionable information about disease transmission to public health decision-makers. Between February and May 2020, experts in infectious disease modeling made weekly predictions about the impact of the pandemic in the U.S. We aggregate...

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Detalles Bibliográficos
Autores principales: McAndrew, Thomas, Reich, Nicholas G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7523166/
https://www.ncbi.nlm.nih.gov/pubmed/32995825
http://dx.doi.org/10.1101/2020.09.21.20196725
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author McAndrew, Thomas
Reich, Nicholas G.
author_facet McAndrew, Thomas
Reich, Nicholas G.
author_sort McAndrew, Thomas
collection PubMed
description During early stages of the COVID-19 pandemic, forecasts provided actionable information about disease transmission to public health decision-makers. Between February and May 2020, experts in infectious disease modeling made weekly predictions about the impact of the pandemic in the U.S. We aggregated these predictions into consensus predictions. In March and April 2020, experts predicted that the number of COVID-19 related deaths in the U.S. by the end of 2020 would be in the range of 150,000 to 250,000, with scenarios of near 1m deaths considered plausible. The wide range of possible future outcomes underscored the uncertainty surrounding the outbreak’s trajectory. Experts’ predictions of measurable short-term outcomes had varying levels of accuracy over the surveys but showed appropriate levels of uncertainty when aggregated. An expert consensus model can provide important insight early on in an emerging global catastrophe.
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spelling pubmed-75231662020-09-30 An expert judgment model to predict early stages of the COVID-19 outbreak in the United States McAndrew, Thomas Reich, Nicholas G. medRxiv Article During early stages of the COVID-19 pandemic, forecasts provided actionable information about disease transmission to public health decision-makers. Between February and May 2020, experts in infectious disease modeling made weekly predictions about the impact of the pandemic in the U.S. We aggregated these predictions into consensus predictions. In March and April 2020, experts predicted that the number of COVID-19 related deaths in the U.S. by the end of 2020 would be in the range of 150,000 to 250,000, with scenarios of near 1m deaths considered plausible. The wide range of possible future outcomes underscored the uncertainty surrounding the outbreak’s trajectory. Experts’ predictions of measurable short-term outcomes had varying levels of accuracy over the surveys but showed appropriate levels of uncertainty when aggregated. An expert consensus model can provide important insight early on in an emerging global catastrophe. Cold Spring Harbor Laboratory 2020-09-23 /pmc/articles/PMC7523166/ /pubmed/32995825 http://dx.doi.org/10.1101/2020.09.21.20196725 Text en http://creativecommons.org/licenses/by-nc-nd/4.0/It is made available under a CC-BY-NC-ND 4.0 International license (http://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Article
McAndrew, Thomas
Reich, Nicholas G.
An expert judgment model to predict early stages of the COVID-19 outbreak in the United States
title An expert judgment model to predict early stages of the COVID-19 outbreak in the United States
title_full An expert judgment model to predict early stages of the COVID-19 outbreak in the United States
title_fullStr An expert judgment model to predict early stages of the COVID-19 outbreak in the United States
title_full_unstemmed An expert judgment model to predict early stages of the COVID-19 outbreak in the United States
title_short An expert judgment model to predict early stages of the COVID-19 outbreak in the United States
title_sort expert judgment model to predict early stages of the covid-19 outbreak in the united states
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7523166/
https://www.ncbi.nlm.nih.gov/pubmed/32995825
http://dx.doi.org/10.1101/2020.09.21.20196725
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