Ensemble

COVID-19 models Ensemble method Scenario projections

Journal

Epidemics
ISSN: 1878-0067
Titre abrégé: Epidemics
Pays: Netherlands
ID NLM: 101484711

Informations de publication

Date de publication:
08 Feb 2024
Historique:
received: 17 08 2023
revised: 19 12 2023
accepted: 06 02 2024
medline: 24 2 2024
pubmed: 24 2 2024
entrez: 23 2 2024
Statut: aheadofprint

Résumé

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 "Ensemble

Identifiants

DOI: 10.1016/j.epidem.2024.100748 PMID: 38394928
pubmed: 38394928
pii: S1755-4365(24)00009-4
doi: 10.1016/j.epidem.2024.100748
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

100748

Informations de copyright

Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest None.

Auteurs

Clara Bay (C)

Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Network Science Institute, Boston, MA, USA.

Guillaume St-Onge (G)

Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Network Science Institute, Boston, MA, USA.

Jessica T Davis (JT)

Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Network Science Institute, Boston, MA, USA.

Matteo Chinazzi (M)

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 (E)

Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA.

Justin Lessler (J)

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 (MC)

U.S. Geological Survey, Eastern Ecological Science Center, Laurel, MD, USA.

Katriona Shea (K)

Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA.

Shaun Truelove (S)

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 (C)

Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.

Alessandro Vespignani (A)

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. Electronic address: a.vespignani@northeastern.edu.

Classifications MeSH