Developed a new scenario ensembling procedure for epidemic scenario projections.

Resulting Ensemble2 models provide better performance and calibration than individual models, effectively encompassing a wide range of plausible outcomes.

Emphasized the significance of scenario design and effective communication in epidemic modeling.

Throughout the COVID-19 pandemic, scenario modeling played a crucial role in shaping the decision-making process of public health policies. Unlike forecasts, scenario projections rely on specific assumptions about the future that consider different plausible states-of-the-world that may or may not be realized and that depend on policy interventions, unpredictable changes in the epidemic outlook, etc. As a consequence, long-term scenario projections require different evaluation criteria than the ones used for traditional short-term epidemic forecasts. Here, we propose a novel ensemble procedure for assessing pandemic scenario projections using the results of the Scenario Modeling Hub (SMH) for COVID-19 in the United States (US). By defining a “scenario ensemble” for each model and the ensemble of models, termed “Ensemble2”, we provide a synthesis of potential epidemic outcomes, which we use to assess projections’ performance, bypassing the identification of the most plausible scenario. We find that overall the Ensemble2 models are well-calibrated and provide better performance than the scenario ensemble of individual models. The ensemble procedure accounts for the full range of plausible outcomes and highlights the importance of scenario design and effective communication. The scenario ensembling approach can be extended to any scenario design strategy, with potential refinements including weighting scenarios and allowing the ensembling process to evolve over time.

Ensemble method

Scenario projections

COVID-19 models

As ground truth data, we use the weekly incident deaths and reported cases from the JHU CSSE group (Dong et al., 2020, Dong et al., 2022) and the weekly incident hospitalizations from HealthData.gov (2023). We use more specifically the formatted version provided by the COVID-19 Forecast Hub (2023). All models’ projections we use are made available by the Scenario Modeling Hub (2023). Our code for the project is publicly available on Zenodo (Bay and St-Onge, 2023).

© 2024 The Authors. Published by Elsevier B.V.