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. 2022 Oct 3;380(2233):20210305.
doi: 10.1098/rsta.2021.0305. Epub 2022 Aug 15.

Refining epidemiological forecasts with simple scoring rules

Robert E Moore  1 , Conor Rosato  1 , Simon Maskell  1
Affiliations

Refining epidemiological forecasts with simple scoring rules

Robert E Moore et al. Philos Trans A Math Phys Eng Sci. .

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'.

Keywords: Bayesian; COVID-19; NSES; forecasting; multisource; scores.

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Figures

Figure 1.
Figure 1.
A graph of the transmission model. Individuals begin their journey in the susceptible (S) state. From here, they are infected and move into the exposed (E) state. After the virus has incubated for a while, they continue into the infectious (I) state. Next, they enter the pending (P) state, after which they either migrate into the recovered (R) state if convalescing or pass into the deceased (D) state if terminally ill.
Figure 2.
Figure 2.
Forecasts generated with point estimates and posterior samples for smoothness hyperparameter, σβ, values of (a) 0.025 and (b) 0.005. (Online version in colour.)
See this image and copyright information in PMC

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