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TRACE-Omicron: Policy Counterfactuals to Inform Mitigation of COVID-19 Spread in the United States.
O'Gara, David; Rosenblatt, Samuel F; Hébert-Dufresne, Laurent; Purcell, Rob; Kasman, Matt; Hammond, Ross A.
Affiliation
  • O'Gara D; Division of Computational and Data Sciences, Washington University in St. Louis.
  • Rosenblatt SF; Vermont Complex Systems Center, University of Vermont.
  • Hébert-Dufresne L; Department of Computer Science, University of Vermont.
  • Purcell R; Vermont Complex Systems Center, University of Vermont.
  • Kasman M; Department of Computer Science, University of Vermont.
  • Hammond RA; Center On Social Dynamics and Policy, Brookings Institution.
Adv Theory Simul ; 6(7)2023 Jul.
Article in En | MEDLINE | ID: mdl-38283383
ABSTRACT
The Omicron wave was the largest wave of COVID-19 pandemic to date, more than doubling any other in terms of cases and hospitalizations in the United States. In this paper, we present a large-scale agent-based model of policy interventions that could have been implemented to mitigate the Omicron wave. Our model takes into account the behaviors of individuals and their interactions with one another within a nationally representative population, as well as the efficacy of various interventions such as social distancing, mask wearing, testing, tracing, and vaccination. We use the model to simulate the impact of different policy scenarios and evaluate their potential effectiveness in controlling the spread of the virus. Our results suggest the Omicron wave could have been substantially curtailed via a combination of interventions comparable in effectiveness to extreme and unpopular singular measures such as widespread closure of schools and workplaces, and highlight the importance of early and decisive action.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Clinical_trials Language: En Journal: Adv Theory Simul Year: 2023 Document type: Article Country of publication:

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Clinical_trials Language: En Journal: Adv Theory Simul Year: 2023 Document type: Article Country of publication: