Refining epidemiological forecasts with simple scoring rules

Moore, RE ORCID: 0000-0001-7572-4984, Rosato, C ORCID: 0000-0001-8394-7344 and Maskell, S ORCID: 0000-0003-1917-2913 (2022) Refining epidemiological forecasts with simple scoring rules Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, 380 (2233). 20210305-. ISSN 1364-503X, 1471-2962

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Abstract

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'.

Item Type:	Article
Uncontrolled Keywords:	Bayesian, multisource, COVID-19, forecasting, scores, NSES
Divisions:	Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	04 Apr 2022 08:04
Last Modified:	22 Jan 2026 10:26
DOI:	10.1098/rsta.2021.0305
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3151913
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