Refining epidemiological forecasts with simple scoring rules.
Bayesian
COVID-19
NSES
forecasting
multisource
scores
Journal
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
ISSN: 1471-2962
Titre abrégé: Philos Trans A Math Phys Eng Sci
Pays: England
ID NLM: 101133385
Informations de publication
Date de publication:
03 Oct 2022
03 Oct 2022
Historique:
entrez:
15
8
2022
pubmed:
16
8
2022
medline:
17
8
2022
Statut:
ppublish
Résumé
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'.
Identifiants
pubmed: 35965461
doi: 10.1098/rsta.2021.0305
pmc: PMC9376716
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
20210305Références
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