Authors
Clara Bay, Kunpeng Mu, Guillaume St-Onge, Matteo Chinazzi, Jessica T Davis, Alessandro Vespignani
Published in
Epidemics. Volume 56. Pages 100929. Jul 10, 2026. Epub Jul 10, 2026.
Abstract
Scoring rules are critical for evaluating the predictive performance of epidemic models by quantifying how well their projections and forecasts align with observed data. In this study, we introduce the energy score as a performance metric for stochastic trajectory-based epidemic models. As a multivariate extension of the continuous ranked probability score (CRPS), the energy score provides a single, unified measure for time-series predictions. It evaluates both calibration and sharpness by considering the distances between individual trajectories and observed data, as well as the inter-trajectory variability. We provide an overview of how the energy score can be applied to assess both scenario projections and forecasts in this format, with a particular focus on a detailed analysis of the Scenario Modeling Hub results for the 2023-2024 influenza season. By comparing the energy score to the widely used weighted interval score (WIS), we demonstrate its utility as a tool for evaluating epidemic models, especially in scenarios requiring integration of predictions across multiple target outcomes into a single, interpretable metric.
PMID:
42462450
Bibliographic data and abstract were imported from PubMed on 17 Jul 2026.
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