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Forecasting efforts from prior epidemics and COVID-19 predictions

Since the onset of the COVID-19 pandemic, countless disease prediction models have emerged, shaping the focus of news media, policymakers, and broader society. We reviewed the accuracy of forecasts made during prior twenty-first century epidemics, namely SARS, H1N1, and Ebola. We found that while di...

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Autores principales: Nadella, Pranay, Swaminathan, Akshay, Subramanian, S. V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7366467/
https://www.ncbi.nlm.nih.gov/pubmed/32676971
http://dx.doi.org/10.1007/s10654-020-00661-0
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author Nadella, Pranay
Swaminathan, Akshay
Subramanian, S. V.
author_facet Nadella, Pranay
Swaminathan, Akshay
Subramanian, S. V.
author_sort Nadella, Pranay
collection PubMed
description Since the onset of the COVID-19 pandemic, countless disease prediction models have emerged, shaping the focus of news media, policymakers, and broader society. We reviewed the accuracy of forecasts made during prior twenty-first century epidemics, namely SARS, H1N1, and Ebola. We found that while disease prediction models were relatively nascent as a research focus during SARS and H1N1, for Ebola, numerous such forecasts were published. We found that forecasts of deaths for Ebola were often far from the eventual reality, with a strong tendency to over predict. Given the societal prominence of these models, it is crucial that their uncertainty be communicated. Otherwise, we will be unaware if we are being falsely lulled into complacency or unjustifiably shocked into action. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10654-020-00661-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-73664672020-07-17 Forecasting efforts from prior epidemics and COVID-19 predictions Nadella, Pranay Swaminathan, Akshay Subramanian, S. V. Eur J Epidemiol Essay Since the onset of the COVID-19 pandemic, countless disease prediction models have emerged, shaping the focus of news media, policymakers, and broader society. We reviewed the accuracy of forecasts made during prior twenty-first century epidemics, namely SARS, H1N1, and Ebola. We found that while disease prediction models were relatively nascent as a research focus during SARS and H1N1, for Ebola, numerous such forecasts were published. We found that forecasts of deaths for Ebola were often far from the eventual reality, with a strong tendency to over predict. Given the societal prominence of these models, it is crucial that their uncertainty be communicated. Otherwise, we will be unaware if we are being falsely lulled into complacency or unjustifiably shocked into action. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10654-020-00661-0) contains supplementary material, which is available to authorized users. Springer Netherlands 2020-07-17 2020 /pmc/articles/PMC7366467/ /pubmed/32676971 http://dx.doi.org/10.1007/s10654-020-00661-0 Text en © Springer Nature B.V. 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Essay
Nadella, Pranay
Swaminathan, Akshay
Subramanian, S. V.
Forecasting efforts from prior epidemics and COVID-19 predictions
title Forecasting efforts from prior epidemics and COVID-19 predictions
title_full Forecasting efforts from prior epidemics and COVID-19 predictions
title_fullStr Forecasting efforts from prior epidemics and COVID-19 predictions
title_full_unstemmed Forecasting efforts from prior epidemics and COVID-19 predictions
title_short Forecasting efforts from prior epidemics and COVID-19 predictions
title_sort forecasting efforts from prior epidemics and covid-19 predictions
topic Essay
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7366467/
https://www.ncbi.nlm.nih.gov/pubmed/32676971
http://dx.doi.org/10.1007/s10654-020-00661-0
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