Refining epidemiological forecasts with simple scoring rules

Estimates from infectious disease models have constituted a significant part of the scientific evidence used to inform the response to the COVID-19 pandemic in the UK. These estimates can vary strikingly in their bias and variability. Epidemiological forecasts should be consistent with the observati...

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Detalles Bibliográficos
Autores principales: Moore, Robert E., Rosato, Conor, Maskell, Simon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2022
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9376716/
https://www.ncbi.nlm.nih.gov/pubmed/35965461
http://dx.doi.org/10.1098/rsta.2021.0305
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author Moore, Robert E.
Rosato, Conor
Maskell, Simon
author_facet Moore, Robert E.
Rosato, Conor
Maskell, Simon
author_sort Moore, Robert E.
collection PubMed
description Estimates from infectious disease models have constituted a significant part of the scientific evidence used to inform the response to the COVID-19 pandemic in the UK. These estimates can vary strikingly in their bias and variability. Epidemiological forecasts should be consistent with the observations that eventually materialize. We use simple scoring rules to refine the forecasts of a novel statistical model for multisource COVID-19 surveillance data by tuning its smoothness hyperparameter. This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.
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spelling pubmed-93767162022-08-16 Refining epidemiological forecasts with simple scoring rules Moore, Robert E. Rosato, Conor Maskell, Simon Philos Trans A Math Phys Eng Sci Articles Estimates from infectious disease models have constituted a significant part of the scientific evidence used to inform the response to the COVID-19 pandemic in the UK. These estimates can vary strikingly in their bias and variability. Epidemiological forecasts should be consistent with the observations that eventually materialize. We use simple scoring rules to refine the forecasts of a novel statistical model for multisource COVID-19 surveillance data by tuning its smoothness hyperparameter. This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’. The Royal Society 2022-10-03 2022-08-15 /pmc/articles/PMC9376716/ /pubmed/35965461 http://dx.doi.org/10.1098/rsta.2021.0305 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Articles
Moore, Robert E.
Rosato, Conor
Maskell, Simon
Refining epidemiological forecasts with simple scoring rules
title Refining epidemiological forecasts with simple scoring rules
title_full Refining epidemiological forecasts with simple scoring rules
title_fullStr Refining epidemiological forecasts with simple scoring rules
title_full_unstemmed Refining epidemiological forecasts with simple scoring rules
title_short Refining epidemiological forecasts with simple scoring rules
title_sort refining epidemiological forecasts with simple scoring rules
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9376716/
https://www.ncbi.nlm.nih.gov/pubmed/35965461
http://dx.doi.org/10.1098/rsta.2021.0305
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