Ensemble2: Scenarios ensembling for communication and performance analysis
Clara Bay,
Guillaume St-Onge,
Jessica T. Davis,
Matteo Chinazzi,
Emily Howerton,
Justin Lessler,
Michael C. Runge,
Katriona Shea,
Shaun Truelove,
Cecile Viboud,
Alessandro Vespignani
Affiliations
Clara Bay
Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Network Science Institute, Boston, MA, USA
Guillaume St-Onge
Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Network Science Institute, Boston, MA, USA
Jessica T. Davis
Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Network Science Institute, Boston, MA, USA
Matteo Chinazzi
Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Network Science Institute, Boston, MA, USA; The Roux Institute, Northeastern University, Portland, ME, USA
Emily Howerton
Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
Justin Lessler
Department of Epidemiology, University of North Carolina Gillings School of Public Health, Chapel Hill, NC, USA; Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA; Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
Michael C. Runge
U.S. Geological Survey, Eastern Ecological Science Center, Laurel, MD, USA
Katriona Shea
Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
Shaun Truelove
Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
Cecile Viboud
Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
Alessandro Vespignani
Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Network Science Institute, Boston, MA, USA; The Roux Institute, Northeastern University, Portland, ME, USA; Corresponding author at: Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Network Science Institute, Boston, MA, USA.
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.
