A multiscale modeling framework for Scenario Modeling: Characterizing the heterogeneity of the COVID-19 epidemic in the US.

Autor:	Chinazzi M; The Roux Institute, Northeastern University, Portland, ME, USA; Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA., Davis JT; Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA., Y Piontti AP; Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA., Mu K; Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA., Gozzi N; Institute for Scientific Interchange Foundation, Turin, Italy., Ajelli M; Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA., Perra N; Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA; School of Mathematical Sciences, Queen Mary University, London, UK., Vespignani A; Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA; Institute for Scientific Interchange Foundation, Turin, Italy. Electronic address: a.vespignani@northeastern.edu.
Jazyk:	angličtina
Zdroj:	Epidemics [Epidemics] 2024 Jun; Vol. 47, pp. 100757. Date of Electronic Publication: 2024 Mar 05.
DOI:	10.1016/j.epidem.2024.100757
Abstrakt:	The Scenario Modeling Hub (SMH) initiative provides projections of potential epidemic scenarios in the United States (US) by using a multi-model approach. Our contribution to the SMH is generated by a multiscale model that combines the global epidemic metapopulation modeling approach (GLEAM) with a local epidemic and mobility model of the US (LEAM-US), first introduced here. The LEAM-US model consists of 3142 subpopulations each representing a single county across the 50 US states and the District of Columbia, enabling us to project state and national trajectories of COVID-19 cases, hospitalizations, and deaths under different epidemic scenarios. The model is age-structured, and multi-strain. It integrates data on vaccine administration, human mobility, and non-pharmaceutical interventions. The model contributed to all 17 rounds of the SMH, and allows for the mechanistic characterization of the spatio-temporal heterogeneities observed during the COVID-19 pandemic. Here we describe the mathematical and computational structure of our model, and present the results concerning the emergence of the SARS-CoV-2 Alpha variant (lineage designation B.1.1.7) as a case study. Our findings show considerable spatial and temporal heterogeneity in the introduction and diffusion of the Alpha variant, both at the level of individual states and combined statistical areas, as it competes against the ancestral lineage. We discuss the key factors driving the time required for the Alpha variant to rise to dominance within a population, and quantify the impact that the emergence of the Alpha variant had on the effective reproduction number at the state level. Overall, we show that our multiscale modeling approach is able to capture the complexity and heterogeneity of the COVID-19 pandemic response in the US. Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze:	MEDLINE
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